Method and system for assessing the trip performance of a driver

ABSTRACT

Method for automatically assessing performance of a driver (110) of a vehicle (100) for a particular trip, wherein current driving data sets, comprising basic driving data are repeatedly read from the vehicle, which method comprises the steps a) collecting previous-trip driving data sets, comprising instantaneous vehicle energy consumption and instantaneous vehicle velocity, for different previous trips, different drivers and different vehicles; b) for a plurality of said previous-trip datasets, calculating a respective relative instantaneous vehicle energy consumption value; c) calculating a characteristic vehicle relative energy consumption function; and d) calculating a value of a trip performance parameter based upon a weighted average value of the respective relative instantaneous energy consumptions for previous-trip data sets that correspond to each of said current-trip data sets, which weighting is performed using said characteristic vehicle relative energy consumption function. The invention also relates to a system.

The present invention relates to a method for assessing the tripperformance of a driver. In particular, the invention relates for suchassessment in relation to a driver of a vehicle, such as a motorvehicle. In some aspects, the invention also relates to such assessmentin relation to a driver of a non-motorized vehicle, such as a bike.Furthermore, the invention relates to a system.

Today, an abundance of data is available electronically during and afterdriving of various vehicles, such as trip computers providinginformation about a current trip performed using a vehicle; standardizeddigital interfaces, such as a CAN-bus based interface, arranged invehicles and arranged to provide vehicle- and driving-related data tohardware appliances pluggable into the vehicle using such interfaces;and data available from standalone portable equipment, such assmartphones and GPS equipment, arranged in the vehicle during use. Suchdata is today used for traffic information purposes, by wirelesslycollecting current driving data for many vehicles, such as using theinternet, and calculating expected travelling times, performing routeplanning and so forth.

At the same time, there is an increasing need, for reasons ofenvironmental concern, economy, risk management, etc., of measuring thedriving performance of individual vehicle drivers and groups of drivers.For instance, by measuring fuel consumption, it may be possible todetermine how environmentally friendly the driving style of a particulardriver is. In the extension, such information may be used to, forinstance, keep track on the total environmental impact of a fleet ofvehicles. Also, such information can be used for feedback purposes, inorder to improve performance over time for individual drivers as well ason an aggregate level.

However, since different vehicles have typical fuel consumptionprofiles, and since identical vehicles can perform very differentlyunder different conditions in terms of load, traffic situation, roadconditions, and so forth, only using fuel consumption is a bluntmeasure. In addition to this, reliable fuel consumption data is notreadily available for many types of vehicles. For non-motorizedvehicles, such as bikes, fuel consumption is not relevant at all as ameasurement value for a particular trip with such vehicle.

Hence, there is a need for a way to more accurately measure drivingperformance of vehicle drivers, in particular for individual trips,which may be used both for comparing relative driving performance fromvarious perspectives between different trips by the same driver/vehiclecombination as well as across several drivers/vehicles.

The present invention solves these problems.

Hence, the invention relates to a method for automatically assessingperformance of a driver of a current vehicle for a particular currenttrip, wherein updated current-trip driving data sets are repeatedly readfrom the vehicle, which current-trip data sets each comprises data fromat least a predetermined set of basic driving data parameters, whereinnew such current-trip data sets are read from the vehicle at consecutiveobservation time points separated by at the most a predeterminedobservation time period, which method is characterised in that themethod comprises the steps of a) collecting previous-trip driving datasets, observed at a plurality of different observation time points, fora plurality of different previous trips made by a plurality of differentdrivers and a plurality of different vehicles, which previous-trip datasets each comprises parameter values for at least a certainpredetermined set of qualified driving data parameters in turncomprising the said basic parameter set and in particular instantaneousvehicle energy consumption and instantaneous vehicle velocity; b) for aplurality of said previous-trip data sets, calculating a respectiverelative instantaneous vehicle energy consumption value, which relativeenergy consumption is relative to a total energy consumption for arespective trip during which the previous-trip data set in question wasobserved; c) calculating a characteristic vehicle relative energyconsumption function regarding the value of said relative instantaneousvehicle energy consumption for different instantaneous vehicle velocityparameter values; and d) calculating a value of a trip performanceparameter based upon a weighted average value of the respective relativeinstantaneous energy consumptions for previous-trip data sets thatcorrespond to each of said current-trip data sets based upon asimilarity or conformance measure regarding the respective values ofsaid basic parameters, which weighting is performed using saidcharacteristic vehicle relative energy consumption function.

The invention further relates to a system for automatically assessingperformance of a driver of a current vehicle for a particular currenttrip, which system is arranged to repeatedly read updated current-tripdriving data sets from the vehicle, which current-trip data sets eachcomprises data from at least a predetermined set of basic driving dataparameters, wherein the system is arranged to read new such current-tripdata sets from the vehicle at consecutive observation time pointsseparated by at the most a predetermined observation time period, whichsystem is characterised in that the system comprises a server, arrangedto collect previous-trip driving data sets, observed at a plurality ofdifferent observation time points, for a plurality of different previoustrips made by a plurality of different drivers and a plurality ofdifferent vehicles, which previous-trip data sets each comprisesparameter values for at least a certain predetermined set of qualifieddriving data parameters in turn comprising the said basic parameter setand in particular instantaneous vehicle energy consumption andinstantaneous vehicle velocity, in that the system is arranged to, for aplurality of said previous-trip data sets, calculate a respectiverelative instantaneous vehicle energy consumption value, which relativeenergy consumption is relative to a total energy consumption for arespective trip during which the previous-trip data set in question wasobserved, in that the system is arranged to calculate a characteristicvehicle relative energy consumption function regarding the value of saidrelative instantaneous vehicle energy consumption for differentinstantaneous vehicle velocity parameter values, and in that the systemis arranged to calculate a value of a trip performance parameter basedupon a weighted average value of the respective relative instantaneousenergy consumptions for previous-trip data sets that correspond to eachof said current-trip data sets based upon a similarity or conformancemeasure regarding the respective values of said basic parameters, whichweighting is performed using said characteristic vehicle relative energyconsumption function.

In the following, the invention will be described in detail, withreference to exemplifying embodiments of the invention and to theenclosed drawings, wherein:

FIGS. 1a-1d are respective simplified views of a vehicle showingrespective parts of a system according to four different embodiments ofthe present invention, which systems are arranged to perform a methodaccording to the present invention;

FIG. 2 is an overview illustration of a system according to the presentinvention, arranged to perform a method according to the presentinvention;

FIG. 3 is a flowchart illustrating a method according to the presentinvention;

FIG. 4 is a flowchart also illustrating a method according to theinvention;

FIG. 5 illustrates a measurement scheme according to the presentinvention;

FIGS. 6A and 6B illustrate a mapping of a particular vehicle to aparticular vehicle class;

FIGS. 7-12 are respective flowcharts illustrating methods according tothe present invention;

FIGS. 13a and 13b show respective characteristic instantaneous relativeenergy consumption curves according to the invention;

FIGS. 14 and 15 are respective flowcharts illustrating methods accordingto the present invention; and

FIGS. 16-17 are simplified illustrations of respective exemplifyingembodiments of the present invention.

The figures share reference numerals for same or corresponding parts.

In general, the present invention relates to a method and a system forautomatically assessing performance of a driver of a current vehicle fora particular current trip.

Herein, the term “performance” relates to a quantifiable, in particularmeasurable and/or calculable, quality of a particular trip driven by aparticular driver, and in particular in relation to the driving of thedriven vehicle as such. A “performance parameter” or “performancemeasure” is a well-defined parameter the value of which is a measure ofthe said quality in some respect of the trip in question. Such qualitymay be in terms of environmental footprint, risk of accidents, driverstress level, vehicle wear or any other quantifiable metric relating tothe driven trip.

Furthermore, herein the term “trip” means a journey performed using aparticular vehicle and as controlled by a particular driver of thevehicle in question. A trip may be a round trip or a single way trip.When a trip starts and ends may be determined manually by the driver,and/or may be determined automatically based upon location or velocitydata, or similar.

A “current vehicle” means a vehicle which has performs, currentlyperforms or will perform a “current trip” in the sense of the presentinvention, namely a trip for which a performance measure is calculatedor is to be calculated according to one or several of the preferredembodiments described herein.

A “vehicle” can be a car, a bus, a truck, a motorcycle, or any othermotorized vehicle, such as a gasoline-, diesel- or gas-propelledvehicle, of a vehicle propelled by any other flammable carbohydrate ornon-carbohydrate based fuel, comprising an explosion motor; or anelectrically powered vehicle comprising an electrical motor and abattery. It may also, in some embodiments, be a non-motorized vehicle,such as a bike, a kickbike or rollerskates. The present invention isalso applicable to trains, airplanes, helicopters, boats and otherpropelled vehicles travelling on the ground, water or in the air. Suchapplications are then implemented in a respective way which analogous towhat is said herein below in relation to cars, bikes, etc.

A “driver”, as used herein, is a person controlling the vehicle inquestion during the trip. Examples comprise the driver of a car or abus, as well as a person cycling on a bike. In some embodiments, a“driver” may also be an autopilot or other human-assisted machine, oreven a machine arranged to drive the vehicle completely independently.Such machine may be software- and/or hardware implemented, as suitable.

According to the invention, updated current-trip driving data sets arerepeatedly read from the vehicle, in particular from a current vehicle.

Such reading can be performed by a piece of hardware equipment which isseparate from the vehicle, such as a piece of equipment which isphysically connected to, and communicates via, a hardware interfaceprovided by the vehicle in question, such as a standardized hardwareinterface for connecting equipment for vehicle diagnosis or similar.This is illustrated in FIG. 1a , in which a piece of hardware 120 isphysically connected to a hardware interface 101 of a vehicle 100 drivenby a driver 110. The piece of hardware 120, which may for instance be aconvention OBD (OnBoard Diagnostics) reader, communicates with aportable electronic device 130, such as a mobile phone, a PDA, a laptopcomputer or similar, using a wireless 121 or wired 122 communicationchannel.

The portable electronic device 130 may preferably be controlled, by thedriver 110, and is preferably a general-purpose programmable computerdevice, such as a conventional smartphone, with wireless communicationcapabilities allowing it to communicate wirelessly with entities locatedoutside of the vehicle 100, such as a base stations 140 of a mobiletelephony network. Preferably, the mobile electronic device 130comprises a SIM (Subscriber Identity Module) card 131 or correspondingfunctionality, using which the portable electronic device 130 identifiesitself to such a mobile network. Preferably, the communication betweenthe mobile electronic device 130 and the base station 140 is a digitalconnection, preferably an internet connection, such as using GPRS, 3G,LTE, 4G or 5G. Preferably, the wireless communication 121 between themobile electronic device 130 and the piece of hardware 120 is a localcommunication, such as NFC, Bluetooth®, WiFi, or similar.

FIG. 1b illustrates an alternative setup, in which a portable electronicdevice is not required, but wherein the said piece of hardware 120itself comprises wireless communication functionality 123, such as GPRS,3G, LTE or WiFi functionality, for communicating with the base station140, or a similar entity. Preferably, such communication is based uponidentification using a SIM card 123, or similar functionality, installedin the piece of hardware 120, and is preferably a digital communication,preferably an internet communication. However, the communication mayalso be through a local wireless or wired communication, such as aBluetooth® or USB interface. In the latter case, communication betweenthe vehicle 100 and the central server 150 will take placeintermittently, such as the vehicle 100 loading up data to the centralserver 150 when parked, such as during charging och refuelling.

FIG. 1c illustrates yet another alternative setup, in which the piece ofhardware 120 is not required either. In this case, the vehicle 100comprises a piece of hardware 102 having a communication 103 meansarranged to carry out communications with the base station 140 asdescribed above, preferably based upon identification using a SIM card104, or similar functionality, installed in the vehicle 100. Preferably,the communication in this case is digital, preferably in the form of awireless internet connection.

FIG. 1a further illustrates a central server 150, which is in contactwith the base station 140, for instance via a mobile telephony operator,and for instance in addition via a conventional internet connection 170.Hence, the central server 150 and the portable electronic device 130 arearranged to communicate with each other, at least the portableelectronic device 130 is arranged to, while arranged in the vehicle 100,provide information to the central server 150 using wirelesscommunication. The corresponding is true regarding the piece of hardware120 in FIG. 1b and the piece of hardware 102 in FIG. 1c , that arecorrespondingly arranged to provide information to the central server150 using wireless communication from the vehicle 100.

Also in FIG. 1d , the central server 150 is present, together with theinternet connection 170 and the base stations 140. However, in FIG. 1d ,there is a server 160 arranged at or in the vehicle 100 also. Namely,FIG. 1d illustrates an alternative or supplementary embodiment, usingsuch a local server 160 is arranged at or in the vehicle 100. In thiscase, depending on the practical embodiment as described in furtherdetail herein below, the said provision of information to the centralserver 150 from a corresponding wireless entity 102, 120, 130, asillustrated in FIGS. 1a, 1b, 1c , may or may not take place.Furthermore, a provision of corresponding information takes place to thelocal server 160, using a wired or wireless communication channelprovided locally in the vehicle 100. In FIG. 1d , the local server 160is illustrated as a standalone server, for exemplifying purposes. It is,however, realized that the local server 160 may be a software componentcomprised in the mobile electronic device 130. The local server 160 mayalso be comprised in any of pieces of hardware 102, 120, 130 or be insuitable wired or wireless communication with any of pieces of hardware102, 120, 130. Moreover, the local server 160 is preferably arranged tocommunicate wirelessly with the central server 150, such as via theportable electronic device 130 and further via base stations 140 and theinternet 170, or using a proprietary, preferably SIM card (or similar)identification based, communication functionality.

In a particularly preferred embodiment, the local server 160 isintegrated in the hardware of the vehicle 100, in which case devices120, 130 are not necessary, but the onboard-vehicle system functionalityis completely self-contained in the vehicle. In that case, the localsever 160 may provide the driver of the vehicle 100 with tripperformance parameter value feedback (see below) based upon driving dataset data in the local database 161, but may only intermittentlycommunicate with the central server 150.

It is realized that, in case a local server 160 is used, a respectivesuch local server 160 may be arranged in several different vehicles.Hence, the system according to the present invention may comprise thecentral server 150 as well as a plurality of local servers 160.

In order for the system to know what vehicle is used, and/or whichdriver is driving what vehicle, during a particular observed trip, it ispreferred that each driver and/or each vehicle has an account, such as auser account, on the server 150, which may be registered ahead of timein a way which is conventional as such. It is further preferred that theuser account is tied to an authenticated session, such as a loginsession using the portable electronic device 130, the vehicle 100, or inanother suitable way, so that the server 150 gains knowledge about whatuser of the system is currently driving the vehicle 100. In acorresponding manner, it is preferred that the vehicle 100 isautomatically identified to the server 150, for instance by the vehicle100 identifying itself automatically to the server 100 based upon aunique vehicle identity, or by the user selecting the current vehiclefrom a list of predefined vehicles for that user.

As mentioned above, the present invention relates to a method and asystem for assessing the driving performance of a driver. Thisassessment is, in general, performed automatically by said system, andin particular by automatic calculations performed mainly in the centralserver 150 and/or in local servers 160. In the following, allcalculations and determinations are performed automatically unless it isexplicitly stated that they involve some type of manual interaction.

As used herein, the term “driving data set” is a set of parameter dataobserved during a particular trip. Preferably, the vehicle performingthe trip is arranged to measure corresponding parameter data, usingsuitable hardware and/or software, substantially at the moment in timeat which the driving data set is read from the vehicle, so that thedriving data set substantially represents real-time, or at least nearreal-time, data regarding the trip in question, at the respective timeor measurement. A “current-trip driving data set” is such a driving dataset read from a current vehicle in relation to a current trip,preferably a current trip which is actually currently being undertakenwhen the reading in question is performed.

Furthermore according to the invention, the said current-trip drivingdata sets each comprises data from at least a predetermined set of basicdriving data parameters. Such parameters constitute measurable dataregarding the progression of the trip in question, in this case thecurrent trip, in particular such data which is measurable by therespective concerned vehicle itself. This will be exemplified below.

The basic parameter set preferably comprises at least 3, preferably atleast 4, and preferably at the most 10, preferably at the most 7,different parameters.

Moreover, new such current-trip data sets are read from the vehicle inquestion at consecutive observation time points separated by at the mosta predetermined observation time period. Preferably, the read updatedcurrent-trip data sets are immediately or at least substantiallyimmediately, upon reading, communicated to the central server 150and/or, depending on the embodiment currently performed, to a localserver 160, as the case may be, and using the above described wiredand/or wireless communication links. It is understood that thecurrent-trip data sets are read by the vehicle 100 and made availablevia interface(s) 101, 103, 121, 122 and/or 123.

The current-trip driving data sets are hence collected for, andpreferably during, a current trip, and are furthermore stored, at leastin aggregate form, in the central server 150 and/or in the local server160 for future reference. For this and other purposes, the centralserver 150 and the local server 160 comprises a respective database 151,161, for digital storage of read driving data sets.

Hence, preferably, parameter values of said basic parameter set areautomatically captured by the vehicle 100 and communicated to the saidportable electronic device 130 arranged at the vehicle 100, whichportable electronic device 130 then communicates, via a wireless link121, 140, said driving data sets to the central server 150.Alternatively, the said parameter values are communicated, via awireless link 103, 121, 123 directly from the vehicle 100 to the centralserver 150.

FIG. 2 illustrates three vehicles 100 a, 100 b, 100 c, of which one maybe a current vehicle, and of which each may be as illustrated in FIGS.1a-1d . Each of the vehicles communicates with the central server 150 asdescribed above.

First Aspect

According to an exemplifying embodiment the present invention,illustrated in FIG. 3 in the form of a flow chart, in a first methodstep the method is started.

In a subsequent method step, previous-trip driving data sets arecollected.

As used herein, the term “previous trip” is a trip which was performed,by a certain vehicle, a “previous vehicle”, at least partly before acurrent trip is conducted, or at least a trip for which driving datasets are available to the central server 150 before the current-tripdriving data sets are collected for analysis. In particular, at leastone data set read in relation to and during a previous trip is readbefore at least one current-trip driving data set is read.

Correspondingly, a “previous-trip driving data set” is a data set of thetype discussed above, read from a vehicle performing a previous trip. Itis realized that a driving data set read by a current vehicle canconstitute a previous-trip driving data set for another current vehicle,at a later point in time, or even for the same vehicle, which at thatlater point in time is a current vehicle.

Such collected previous-trip driving data sets are observed at aplurality of different observation time points, for a plurality ofdifferent previous trips made by a plurality of different drivers and aplurality of different vehicles. In particular, it is preferred thatsaid previous-trip driving data sets are observed at at least 1 000,more preferably at least 10 000, more preferably at least 100 000,different observation time points, and/or for at least 100, morepreferably at least 1 000, more preferably at least 10 000 previoustrips, and/or made by at least 5, preferably at least 50, morepreferably at least 100, different drivers and/or at least 5, preferablyat least 50, more preferably at least 100, more preferably at least 1000, different vehicles.

In other words, the previous-trip driving data sets collected preferablyconstitute a large amount of data regarding different trips, driversand/or vehicles.

The collecting of the previous-trip driving data sets may take place bythe vehicle 100 a, 100 b, 100 c in question, as described above withrespect to the collecting of current-trip driving data sets, whichcollecting comprises reading by the vehicle in question, communicatingto the central server 150 and/or a local (arranged in the vehicle inquestion) server 160, and subsequent storage therein. In case a localserver 160 is used, the stored information is subsequently provided tothe central server 150, using a suitable communication method, such aswirelessly via base stations 140, intermittently or after the previoustrip is finished. Hence, the central server 150 will eventually receiveand centrally store, in database 151, a number of previous-trip drivingdata sets for each previous trip conducted using the method and systemaccording to the present invention.

Each of said previous-trip driving data sets comprises respectiveparameter values for at least a certain predetermined set of qualifieddriving data parameters. Such parameters constitute, similarly to theabove mentioned basic driving data parameters, measurable data regardingthe progression of the trip in question, in this case the previous trip.

The qualified driving data parameter set comprises the basic parameterset, which basic set is hence a subset of said qualified set. The basicand qualified sets may also be identical. In particular, the qualifieddriving data parameter set comprises instantaneous vehicle energyconsumption. Preferably, however, the basic driving data parameter set,as opposed to the qualified driving data parameter set, does notcomprise instantaneous vehicle energy consumption.

Said instantaneous vehicle energy consumption may be, for instance,instantaneous fuel consumption or instantaneous power use of a batteryused to propel the vehicle in question. Preferably, the instantaneousvehicle energy consumption is measured and expressed in relation totravelled distance, such as “litres per km” or “Wh per km”, or thecorresponding, even though in other embodiments they could also beexpressed in relation to time, such as “litres per hour”.

The above-described collecting of current-trip driving data sets may beperformed in parallel to the collecting of previous-trip data sets, orafterwards.

In a subsequent method step, which may be performed in parallel to thesaid collecting of previous-trip driving data sets, the collectedprevious-trip driving data sets are grouped, or classified, into basichistoric groups of such sets. Preferably, each previous-trip data set isclassified into at the most one, preferably exactly one, of said basichistoric groups. In this classification, the said historic groups henceconstitute the classes into which the driving data sets are classified.It is noted that these “classes” are not the same as the vehicle classesdescribed below.

Preferably, the said grouping is based upon a basic driving data setsimilarity measure, in other words a comparison measure for comparingdriving data sets comprising said basic parameter set, and determining asimilarity between such compared data sets. Preferably, the basicdriving data set similarity measure is also arranged to comparequalified driving data sets one to each other, or even to compare basicdriving data sets to qualified driving data sets, based upon the valuesof said basic parameters comprised in such data sets. However, it ispreferred that the basic driving data set similarity measure does nottake instantaneous energy consumption data in said driving data setsinto consideration for the calculated similarity measure. Furtherpreferably, the same basic driving data set similarity measure is usedfor all similarity calculations between driving data sets as describedherein.

“Similarity”, as used herein with respect to two driving data sets,refers to numerical similarity of the respective parameter values of thedriving data sets in question.

Hence, after such classification in this method step, a number of basichistoric groups will exist, each comprising zero or more previous-tripdriving data sets that are sufficiently similar one to the otheraccording to the said similarity measure. It is also possible that thesaid classification is performed continuously, so that newly collectedprevious-trip driving data sets are classified into one of said basichistoric groups in connection to their collecting, or intermittently. Inthis case, the contents of said basic historic groups will bedynamically updated as time goes by.

In practise, each previous-trip driving data set may not be individuallystored in the database 151. Instead, the previous-trip driving data setsfor each particular basic historic group are preferably stored in anaggregated manner in the database 151. This may, for instance, beachieved by the definition of each basic historic group beingassociated, in the database 151, with corresponding aggregate datacalculated based upon the previous-trip driving data sets being mappedto the basic historic group in question. Such aggregate data maycomprise, for instance, a group performance parameter value (see below).

In a subsequent method step, for each of said current-trip driving datasets collected as described above, the current-trip driving data set inquestion is mapped to at the most one particular of the above-describedbasic historic groups, hence the same set of basic historic groups usedfor classification of the previous-trip driving data sets.

This mapping, of the current-trip driving data sets to said basichistoric groups, is based upon a basic group conformity measure betweena driving data set and a basic historic group. Even though the saidconformity measure is a measure of conformity between a data set and agroup, in other words how closely the data set in question conforms tothe group in question, whereas the above discussed similarity measure isa measure of similarity between one data set and another data set, theconformity measure can be similar to the similarity measure, or evenanalogous in the sense that the requirements for two data sets to beclassified into one and the same particular group are the same as therequirements for a certain data set to be classified into the group inquestion. In particular, it is preferred that both the conformitymeasure and the similarity measure is based upon a basic historic groupdefinition, so that “conformity” between a data set and a group meansthat the data set falls under the definition of the group, whereas“similarity” between two data sets means that both data sets fall underthe definition of one and the same group (regardless which such group).This will make possible a calculationally simple implementation,resulting in a high performance system.

According to a particularly preferred embodiment, the basic driving dataset similarity measure is arranged to classify driving data sets intoone of a plurality of different predetermined basic historic groupsbased upon conformance of the respective basic parameter valuescomprised in the driving data set in question to respective allowedparameter value ranges for each of said parameters. In particular, foreach parameter in said basic set of parameters and for each basic group,a predefined respective parameter value range is defined. Then, thebasic group is defined in terms of a combination of one such parametervalue range for each parameter in the basic set of parameters. It isnoted that not all parameters in said basic set of parametersnecessarily have to be used, in other words one or several parameterscan have very large allowed intervals. Preferably, at least two, morepreferably at least three, parameters in the basic set of parameters areassociated with consecutive, non-overlapping intervals, and thatdifferent groups are defined by unique combinations of such mutuallynon-overlapping intervals of parameter values. It is preferred that, foreach such parameter, there are at least ten, preferably at least fifty,such non-overlapping intervals.

It is realized that other similarity measures can also be used, such assome type of geometric distance measure based upon the numerical valuesof the parameter values in each data sets, in this case viewed as avector of values.

In a manner corresponding to the above described, with respect to thesimilarity measure, the basic group conformity measure is arranged toclassify driving data sets into one of said plurality of differentpredetermined basic historic groups based upon conformance of therespective basic parameter values comprised in the driving data set inquestion to respective allowed parameter value ranges for each of saidparameters. Preferably, the intervals used to define the basicconformity measure are the same intervals used to define the said basicsimilarity measure.

It is preferred that the said conformity measure is based upon thenumerical parameter values of the current-trip driving data set inquestion and upon a definition of the basic historic group in question.

In subsequent method steps, a first energy consumption-based tripperformance parameter value is calculated for the current trip.

Preferably, the first energy consumption-based trip performanceparameter is calculated based upon a respective energy consumption-basedgroup performance parameter value for each of the basic historic groupsto which at least one current-trip driving data set was mapped asdescribed above. Hence, in a first of said subsequent method steps, orbeforehand, such a group performance parameter value is calculated forat least each such mapped basic historic groups.

It is realized that both the above-described grouping of previous-tripdriving data sets into basic historic groups; mapping of current-tripdata sets to said groups; and/or calculation of said group performanceparameter values can be performed on the fly, continuously as new databecomes available. It is preferred that the said group performanceparameter value always takes into consideration all previous-tripdriving data sets available to the entity performing the saidcalculation, however not that the current-trip driving data set valuesare used as previous-trip driving data sets before the current trip isfinished. Upon finishing, the current trip may become a previous tripfor a subsequent current trip, of the same or another vehicle and/oruser.

For each mapped basic historic group, the said group performanceparameter value is calculated based upon the respective instantaneousenergy consumption value in the respective previous-trip driving datasets classified into, and therefore comprised in, the basic historicgroup in question. It is noted that the previous-trip driving data sets,each comprising said qualified parameter set, comprise such a respectiveinstantaneous energy consumption value.

Using such a method and such a system, driving-related data from manydifferent previous trips, partaken using many different vehicles and bymany different drivers, can be used to automatically assess the drivingperformance of a current trip in a way which does not require anydetailed assumptions of the conditions under which the data wascollected. In particular, it is possible to obtain a surprisinglyaccurate view on the performance of the driver, in terms of drivingenergy consumption, under very shifting external conditions, in terms offor instance vehicle load, road and weather conditions. Furthermore, itis possible to compare the obtained energy consumption performanceacross different drivers and also across different types of vehicles.

Furthermore, it is possible to achieve these advantages even without anya priori information regarding neither geography, nor road or trafficconditions. Hence, no expensive measurements are necessary; instead, alldrivers using the system create a common set of data as they use thesystem, irrespectively of the details describing the externalenvironment in which they perform when doing so.

All these advantages are achievable automatically, without any manualintervention and simply by using the system. This will be explained infurther detail below.

In the embodiment described above, this is possible by the use of theabove-explained basic historic groups, that are used to disconnect theidentity of the previous trips from the previous-trip driving data setsobserved during the previous trip in question, and that allow using theinformation content in the said data sets irrespectively of the otherproperties of each previous trip. In particular, this can be achieved invarious different aspects, using various detailed techniques as will bedescribed in the following. In some of these aspects, it is not vitallyimportant to use basic historic groups, as will become clear.

In particular, the present inventor have discovered that, by fragmentinga large number of previous trips into small segments, where each segmentis so small so that essentially no qualitative information can bederived about the driving from such individual segment, and then mappingthe segments of a current trip to such historically collected segments,very accurate information can be derived, in the aggregate, about thecurrent trip.

It is preferred that the calculations described herein are performedusing and based upon all, or substantially all, available data from alltrips performed by all vehicles that are connected to the system. Inthis case, the calculations must be performed by an entity havingaccess, in aggregate or detailed form, to all such data. It is preferredthat this entity is the central server 150, which then receivesprevious-trip and current-trip driving data sets from all connectedvehicles, either continuously or intermittently, or differently fordifferent connected vehicles, and performs the calculations describedabove. It is also possible that the local server 160 receives theprevious-trip driving data sets, or corresponding data in aggregateformat, such as basic historic group definitions together withcalculated, preferably updated, respective group performance parametervalues, from the central server 150, and then performs the actualcalculation of the said first performance parameter for the current tripperformed by the current vehicle in which the local server 160 isarranged. In this case, updated data can be provided to the local server160 only intermittently, such as before each current trip, once a day oreven once a week, allowing the advantages of the present invention to beachieved even when there is no reliable internet connection available tothe current vehicle, or when driving abroad without any roaming-basedwireless internet connection. The said data may even be provided to thelocal server 160 only once, such as in connection to an installation ofa piece of local server 160 software.

According to a preferred embodiment, the said first group performanceparameter is a relative instantaneous energy consumption value for therespective previous-trip driving data sets in the basic historic groupin question. In particular, it is preferred that this relative value iscalculated in relation to a respective total energy consumption for thecomplete previous trip during which the previous-trip driving data setin question was observed, such as a total petrol, diesel, gas orelectricity consumption or a total average petrol, diesel, gas orelectricity consumption per km of the whole previous trip. In this case,both instantaneous energy consumption data, as well as total energyconsumption for a whole trip, are available from the previous vehicle inquestion, for reading and submission to the central server 150.

FIG. 4 illustrates how this relative instantaneous energy consumptionbased group performance parameter can be calculated.

In a first method step, previous-trip driving data sets are collectedfor a particular previous trip. Once all previous-trip driving data setshave been collected for the previous trip in question, the total energyconsumption for the entire previous trip is also available from theprevious vehicle, for reading and collecting as described above.

Then, for each such collected previous-trip data set for sail previoustrip, a corresponding basic group is identified, and the relativeinstantaneous energy consumption value for the data set in question iscalculated, for instance by dividing the instantaneous energyconsumption value for the data set in question with the total energyconsumption for the entire previous trip in question.

Then, the group performance parameter for the said basic historic groupis updated using the corresponding previous-trip driving data set basicparameter values. This may, for instance, be performed so that the groupperformance parameter value is always an average value of the respectivecalculated relative instantaneous energy consumption values for eachprevious trip data set that has, up to that point in time, been allottedto the basic group in question. For instance, this may be performed by,for each basic historic group, keep track of the number of previous-tripdriving data sets that have been allotted to the group in question, andto perform a suitable weighted average calculation when updating thesaid relative energy consumption value for the group.

In parallel to the constantly ongoing collecting and evaluating ofprevious trips and their respective data sets, as well as relativeinstantaneous relative energy consumptions for various allotted basichistoric groups, current-trip driving data sets are collected asdescribed above. Once the first performance parameter value is to becalculated, the collected current-trip driving data sets are mapped torespective basic groups, as also described above, and the firstperformance parameter is then calculated based upon the calculated groupperformance parameters. This may, for instance, take place bycalculating an average value of the respective group performance valuesfor all mapped groups for the current trip. Such average value may be asimple geometrical average, or, preferably, a weighted average in whichthe respective group performance values of more frequently updated (byprevious trip data sets being allotted thereto) basic groups are givenmore weight than respective group performance values of less frequentlyupdated basic groups. In case not all current-trip driving data setscorrespond to a respective existing basic historic group, the averagingfunction may ignore those current-trip driving data sets for the purposeof calculating the first performance parameter value.

Hence, in this preferred example, the first trip performance parameteris calculated based upon an average value of the respective relativeinstantaneous energy consumption values for the respective basichistoric groups to which the respective current trip driving data setsof the current trip have been mapped. The relative instantaneous energyconsumption value, in turn, for each basic historic group in question,is calculated based upon an average of the respective relativeinstantaneous energy consumption values for each previous-trip data setallotted to the basic historic group in question, as measured inrelation to a corresponding previous trip during which the data set inquestion was observed.

The method according to the present invention can be made completelyautomatic, collecting driving data sets for all trips performed by allvehicles connected to the system according to the present invention.However, in order to increase data quality and decrease adverse effectsdue to data noise, it is preferred that only a subset of said vehiclesare marked as trusted by the system. In this case, the above describedgroup performance parameter values are calculated so that they are notaffected by instantaneous energy consumptions reported by vehicles notmarked as trusted. For such non-trusted vehicles, driving data sets maybe still be collected, but such collected driving data sets do notaffect the group performance parameters for the various basic historicgroups to which the driving data sets are allotted as described above.Also, non-trusted vehicles may constitute current vehicles, andcurrent-trip performance parameter values may be calculated for suchnon-trusted vehicles. Which vehicles that are to be trusted may, forinstance, be manually selected based upon knowledge about data qualityavailable from particular vehicles and possibly also for particulardrivers; automatically selected based upon data availability for apredetermined minimum data type set for vehicles; or in any other way.

Preferably, the predetermined observation time period mentioned above isrelative short, so that each current trip would typically result in alarge plurality of different current trip driving data sets. Preferably,the predetermined observation time period is at the most 10 seconds,preferably at the most 5 seconds, more preferably at the most 2 seconds,more preferably between 0.2 and 2 seconds, most preferably about 1second. Using such short time intervals strikes a good balance betweencollecting as much relevant data as possible while not giving rise tounnecessarily large amounts of data to communicate, store and process.

In particular, it is preferred that the current trip driving data setsare read at regular time intervals, so that the time period between twoconsecutive readings is substantially the same for all pairs of suchconsecutive readings.

The reading as such may be an instantaneous readout or an average valueread across a averaging certain time period, which is preferably at themost 5 seconds of length, preferably at the most 2 seconds of length.

It is noted, firstly, that the collecting of the data sets may beperformed more intermittently, and also at a certain delay, as long asthe reading of driving data sets are performed regularly. Secondly, thedata set reading frequency may or may not be different from the samplingtime period length for each read data set. For instance, if readingstake place every 1 seconds, each reading may relate to vehicle parameterdata covering a respective historical time period of 5 seconds runningup to the currently read second. Such prolonged sampling time period maybe achieved by the vehicle 100 itself, but preferably software and/orhardware implemented logic performing such prolonged sampling iscomprised in either of devices 120, 130, or alternatively in the centralserver 150 or even in the local server 160. In the latter case, thesampling may in practise take place by either device 120, 130, 150, 160receiving repeated instantaneous readings, and performing prolongedsampling readings artificially by performing calculations based uponsuch repeated readings.

Correspondingly, the same is preferred as concerns previous-trip drivingdata sets. Preferably, current-trip and previous-trip driving data setsare read in substantially the same way, using the same observation timeperiods.

Second Aspect

According to one aspect of the present invention, the above discussedbasic parameter set comprises instantaneous vehicle velocity,instantaneous vehicle engine rotation speed, instantaneous vehiclevelocity change as well as instantaneous vehicle engine rotation speedchange. It is then preferred that all these parameter values, for eachdata set in question, are used for calculating said basic groupconformity measure, preferably as well as said basic similarity measure.The said instantaneous vehicle velocity and instantaneous vehicle enginerotation speed are preferably measured on the engine of the vehicle, andpreferably by the vehicle itself, as opposed to being measured using asystem which is not connected to the engine of the vehicle, such asusing a GPS-enabled measurement device or similar. GPS-basedmeasurements are hence preferably not used in this context, but only forproducing the below-described extended driving data sets. Said velocitychange and engine speed change are also, in a similar manner, eithermeasured on the engine or calculated based upon said instantaneousvelocity and engine speed values measured on the engine. Herein, theexpression “measured on the engine” encompasses also other measurementsperformed directly on the vehicle hardware as such, for instancemeasurements performed on wheels or wheel axes of the vehicle.

Herein, for wheeled vehicles using an explosion motor for propelling ofthe vehicles, the relationship between instantaneous vehicle velocityand instantaneous engine rotation speed, and in applicable cases therespective absolute values of these two parameters and also theirrespective changes over time, have proven to be very useful to considerfor the present purposes. However, for electrically propelled wheeledvehicles, it is, as an alternative, possible to instead of instantaneousvehicle speed use instantaneous energy consumption, such asinstantaneous electrical power usage of the electrical motor propellingthe vehicle, as provided by a battery in the vehicle. For the sameelectrical vehicles, instantaneous motor load should then be usedinstead of instantaneous motor rotation speed. Correspondingly, and asapplicable, instantaneous energy consumption change and instantaneousmotor load change should then be used instead of instantaneous vehiclespeed change and instantaneous engine rotation speed change. Of course,for some vehicle types comprising both an explosion engine and anelectrical motor, both these options can be used at the same time. Inparticular, and especially for electrically propelled vehicles, it ispreferred that the basic parameter set comprises, in addition to saidinstantaneous energy consumption, instantaneous energy consumptionchange, instantaneous motor load and instantaneous motor load change,also use instantaneous vehicle velocity, and preferably alsoinstantaneous vehicle velocity change. The latter two can be asdescribed above. It is noted that, for all these values, they arepreferably measured on the vehicle as described above.

Herein, whenever instantaneous vehicle speed and instantaneous enginerotation speed, or the corresponding change measures, are used for somepurpose, it is in general the case that, instead or in addition to thesevalues, as applicable, instantaneous motor load and instantaneous energyconsumption may be used correspondingly. This applies both to thepresent aspect, the below described class-defining parameters andelsewhere in this description.

The present inventor have discovered that it is sufficient to use thesefour basic parameters in order to achieve very reliable data in terms ofthe said current-drive performance parameter. In particular, this istrue in case very many previous-trip driving data sets are used for manydifferent vehicles and/or many different drivers, as quantified above.

Since the current instantaneous vehicle velocity, as well as the currentinstantaneous engine rotation speed, are typically available for readoutfrom the vehicle, they can be readily collected. The vehicle velocitychange and the engine speed change can be readily calculated based uponthe said read values, such as by a software and/or hardware implementedlogic in any of devices 120, 130, 150 or 160.

Even more preferably, no other data values, apart from the said datavalues regarding instantaneous velocity, instantaneous engine rotationspeed, as instantaneous velocity change and instantaneous enginerotation speed change, are used by said basic group conformity measure,and preferably the corresponding is also true for said basic similaritymeasure. This provides for a particularly simple data collecting andperformance parameter calculation process, which still is able toprovide a high quality output.

According to a preferred embodiment, the said instantaneous velocitychange is measured over a certain velocity change time period, so thatthe velocity change is measured as a velocity difference between twopoints in time separated by said time period.

Correspondingly, the said instantaneous engine rotation speed change ispreferably measured over a certain speed change time period, and hencemeasured as a difference in instantaneous engine speed between twopoints in time separated by said time period.

In particular, it is preferred that the length of the velocity changetime period is different from the length of the speed change timeperiod, while, for each previous-trip driving data set the correspondingvelocity change time period and the corresponding speed change timeperiod are overlapping. Preferably, the point in time at which theinstantaneous vehicle velocity is measured and the point in time atwhich the instantaneous engine speed is measured are both, independentlyof each other, contained in both said overlapping change time periods,and preferably measured at the same or substantially the same time. Suchoverlapping and containing guarantees that the measured parameters ofeach driving data set are related to one and the same driving situation,which is important even for very frequently measured driving data sets.

Moreover, the said instantaneous vehicle velocity change time period andsaid instantaneous engine speed change time period are of differentlengths. Namely, in many applications it is necessary to fine-tune eachof said change time periods to capture relevant data regarding the tripin question, and in general the optimal change time periods will not bethe same for different parameters. For instance, it is in generalpreferred that the engine velocity change time period is shorter, suchas at least twice as short, as the vehicle velocity change time period.This is not only due to the fact that engine velocity can change quickerduring driving than vehicle velocity, but also since the use of suchshorter change time period results in the capability of the system tomore accurately capture certain driver behaviour in certain situationswhile driving.

Even in case the velocity time period and the speed time periods havedifferent lengths, it is preferred that, for each observation time pointand hence for each driving data set, they share the same starting timepoint, or, alternatively the same ending time point.

Furthermore, it is preferred that at least one of said velocity changetime period and said speed change time periods have a length which islonger than the above discussed predetermined time period (the timeperiod between each consecutive observation time point).

Both the velocity change time period and the engine speed change timeperiod may have an end point at the corresponding observation timeperiod, and hence correspond to a measurement conducted in thehistorical time period running up to the measurement time of theinstantaneous vehicle velocity and the engine speed. However, it ispreferred that the vehicle velocity change time period runs from theobservation time point of said instantaneous vehicle velocity andforwards, and/or that that the engine speed change time period runs fromthe observation time point of said instantaneous engine rotation speedforwards. This results in that each driving data set comprisesinformation regarding the current situation in terms of instantaneousvelocity and engine speed, as well as how that situation is changedduring the coming time period. The present inventor have discovered thatthis provides very useful performance parameter values for the purposesdiscussed below.

In case the observation time period is up to about 2 seconds, it isparticularly preferred that the vehicle velocity change time period foreach observation time point starts at the instantaneous vehicle velocityobservation time point and runs forwards, between 3 and 10 seconds, andthat the engine speed change time period for each observation time pointstarts at the instantaneous engine speed observation time point and runsforwards, between 1 and 5 seconds.

FIG. 5 illustrates an exemplifying measurement scheme for use with avehicle and the system according to the present invention. Along thetime axis, a number of consecutive observation time points OT1, [ . . .], OT5 are shown, each separated by an observation time period OTP offixed length.

For each observation time point, the following readings are made fromthe vehicle:

-   -   Instantaneous readings (IR1, [ . . . ], IR5]) regarding        instantaneous vehicle velocity, instantaneous engine speed, and        any other instantaneously measured values.    -   Engine speed change (ESC1, [ . . . ], ESC4). This parameter is        measured forward, across a time period which is identical to the        observation time period OTP. Hence, the engine speed change        value for observation time point PT1 will not be available until        observation time point OT2, and can then be collected as        described above.    -   Vehicle velocity speed change (VVC1, [ . . . ], VVC4). This        parameter is measured forward, across a time period which is        longer than the observation time period OTP. Hence, as seen in        FIG. 5, the vehicle velocity speed change value for observation        time point OT1 will not be available until sometime between        observation time point OT3 and observation time point OT4, and        can then be collected as described above.

Preferably, each driving data set is not collected and used to update arespective basic historic group, as described above, until all parametervalues are available for the driving data set in question.

Apart from instantaneous vehicle velocity and engine speed, and vehiclevelocity- and engine speed change, other parameters may also bemeasured, and may also belong to said basic parameter set. Suchparameters comprise instantaneous break (either binary on/off or a breakforce value); instantaneous altitude; altitude change; instantaneous GPSlocation, altitude or heading, and/or GPS altitude or heading change,and/or GPS altitude acceleration; GPS-coordinate based vehicle velocityand/or acceleration; instantaneous engine oil temperature; gear numberused; vehicle blinkers activated; outdoors temperature; status of cruisecontrol systems; and/or any other data which is available either fromthe vehicle 100 itself or from the mobile device 130 and sensorsarranged therein, preferably data that in some respect quantifies theposition, behaviour and/or internal state of the vehicle.

In particular, it is preferred that break information, at least in theform of a binary signal (break activated/not activated) is part of saidbasic parameter set, and is hence also read from the vehicle at eachobservation time point.

Correspondingly, the said basic similarity and/or conformance measurescan take into consideration additional parameters of the exemplifiedtypes, using the corresponding approach as described above. Forinstance, in case break information is used in said measure, the binarybreak value (on/off) may be one of the defining parameters of said basichistoric groups, and two driving data sets may be allotted to differentbasic historic groups in case the driving data sets are identical apartfrom a difference in break parameter value.

As described above, there is a communication from the current vehicle100 to the central server 150, comprising current-trip driving datasets. In addition thereto, according to a preferred embodiment, thecurrent-trip performance parameter value is calculated, preferably asdescribed above by the central server 150, and thereafter communicated,via the above described wireless link, from the central server 150 tothe current vehicle 100, such as to the portable electronic device 130arranged at the current vehicle 100, and presented to the currentdriver. This presentation will be exemplified below.

Such calculation, together with possible communication and presentationto the current driver, may be performed in connection to a current tripbeing completed. However, according to a preferred embodiment a value ofthe above described current-trip performance parameter is calculatedrepeatedly, preferably at least every 10 minutes, more preferably atleast every 2 minutes, more preferably at least every 30 seconds, duringthe current trip. Then, it is preferred that, for the purposes ofcalculating the said current-trip performance parameter value, thecurrent trip is considered to be that part of the current trip which hastaken place, and has been collected, up to the moment at which the valueof the current-trip performance parameter is calculated. Hence, theperformance parameter value is calculated as if the collectedcurrent-trip driving data sets up to the point of calculation of theperformance parameter value constitute data of the entire, completedcurrent trip. In this case, it is preferred that the currentlycalculated such performance parameter value is communicated to thecurrent vehicle and presented to the driver upon each such calculation.This way, the current driver can be provided with regularly updatedinformation regarding the performance of the current trip, which makesit possible for the current driver to adjust his or her driving style inresponse to such information fed back from the system according to thepresent invention.

In case the current vehicle lacks an active internet connection, thecollected current-trip driving data sets may be stored locally in thevehicle during the trip, for subsequent upload to the server 150 once aninternet connection is again available. Then, the performance parametervalue can be calculated and provided to the user in connection to thislater point in time.

Third Aspect

In one aspect of the present invention, illustrated in FIG. 12, arespective instantaneous relative vehicle energy consumption value iscalculated for a plurality of the previous-trip data sets as describedabove. In particular, throughout the description of this aspect, thisrelative energy consumption is relative to a total energy consumptionfor a respective trip during which the previous-trip data set inquestion was observed.

Hence, in a first step, previous-trip and current-trip driving data setsare collected, and the collected current-trip driving data sets are eachmapped to respective previous-trip driving data sets in a suitable way,such as using the said basic similarity measure, and/or using basichistoric groups and the basic conformance measure, as described above.

In a second step, which may be performed at any time before a thirdstep, and in particular before, during or after the said first step, acharacteristic relative vehicle energy consumption function, regardingthe value of said instantaneous relative vehicle energy consumption fordifferent instantaneous vehicle velocity parameter values, iscalculated. This characteristic function is preferably calculated basedupon available previous-trip driving data sets for previous vehicles asexplained below. Preferably, there is maximally one such characteristicfunction for each of the below described vehicle classes, and it ispreferred that each characteristic function is updated automatically asnew previous-trip driving data sets become available, or at leastintermittently based upon newly available data. This way, an automaticcompensatory mechanism is accomplished without adding more than limitedcalculation overhead to the method.

The characteristic vehicle relative energy consumption function ispreferably not calculated only based upon data observed for one vehicle,such as the current vehicle. Instead, it is preferably calculated basedupon data observed for a plurality of previous vehicles. Thecharacteristic function may be calculated taking into considerationprevious-trip driving data sets for substantially all, or at least aplurality of, the vehicles in the same vehicle class as the one to whichthe current vehicle is mapped and to no other vehicles; alternatively itmay be calculated based on previous-trip driving data sets forsubstantially all, or at least a plurality, of all the previous vehiclesregardless of vehicle class.

In said third step, the value of a trip performance parameter, such asthe above first or second trip performance parameter, is calculated, forinstance as described above. In particular, the performance parameter iscalculated based upon an average value of the respective relativeinstantaneous energy consumptions for previous-trip data sets thatcorrespond to each of said current-trip data sets based upon asimilarity or conformance measure regarding the respective values ofsaid basic parameters.

In case classes are used (see below), the current vehicle is hence firstclassified into a particular current class of the below described set ofclasses based upon said class conformity measure, and the tripperformance parameter value is then calculated based upon only the saidrespective relative instantaneous vehicle energy consumption values forprevious-trip data sets in the current collection, corresponding to thecurrent class as defined below.

According to the present aspect of the invention, however, the saidaverage is a weighted average wherein the weighting is performed usingsaid characteristic vehicle relative energy consumption function.

Namely, the characteristic vehicle relative energy consumption functiondescribes a characteristic relationship between instantaneous velocityand instantaneous relative energy consumption for previous vehicles. Inother words, for each of a plurality of instantaneous velocity values orintervals, the characteristic function provides a value of acharacteristic or typical relative energy consumption value for thevehicle velocity in question, where each such relative energyconsumption value is a relative energy consumption for previous-tripdriving data sets describing the said instantaneous vehicle velocity andin relation to a total energy consumption during the complete tripduring which such a previous-trip driving data set was observed.

When using this characteristic function in order to perform a weightedaverage calculation with respect to the relative energy consumptions ofeach previous-trip driving data set corresponding to each of thecurrent-trip driving data set, the result is that systematic artefactsrelated to relative energy consumption for different vehicle velocitiesare automatically corrected for, and the reliability of the resultingtrip performance value is increased as a result. Examples of possiblesystematic artefacts comprise systematically high relative energyconsumption values at low velocities, due to internal engine friction,as well as systematically high relative energy consumption values athigh velocities, due to air friction. However, other artefacts may alsooccur, such as artefacts only occurring in particular vehicle classesand so forth.

Preferably, the characteristic vehicle relative energy consumptionfunction is normalized, so that its mean value, for all occurringvehicle velocity values or intervals, is 1. This type of curve, which isexemplified in FIGS. 13A and 13B, provides for a simple weighted averagecalculation, in which a simple multiplication with the characteristicvehicle relative energy consumption function is often sufficient.

As seen in FIGS. 13A and 13B, the average value of the characteristicvehicle relative energy consumption function CHAR, as seen across thewhole allowable or used vehicle velocity range, averages to 1, asindicated by the horizontal line “1”.

FIG. 13A is a continuous function, which may be produced by, forinstance, adjusting a polygon function of suitable power to best fit adata set comprising, for said previous-trip driving data sets, allobserved value pairs (instantaneous vehicle velocity; instantaneousrelative energy consumption), and then normalizing the function. Then,for each instantaneous vehicle velocity, a respective characteristicrelative energy consumption is indicated by the characteristic function.

FIG. 13B illustrates an alternative way of calculating thecharacteristic function, in which the function CHAR is a step functioncorresponding to the one illustrated in FIG. 13A. This approach isparticularly advantageous when using the above described interval basedbasic group conformance measure and driving data set similaritymeasures. Hence, for each of a number of, preferably non-overlapping andpreferably predetermined, vehicle velocity intervals (illustrated inFIG. 13B using vertical lines), the function specifies a respectivecharacteristic instantaneous relative energy consumption value. Apartfrom this difference, the curve illustrated in FIG. 13B is used in a waywhich fully corresponds to the curve in 13A.

In particular, it is preferred that the characteristic vehicle relativeenergy consumption function is calculated based upon an average relativeinstantaneous vehicle energy consumption for several previous-trip datasets having the same vehicle velocity. In this context, “the samevelocity” encompasses velocities belonging to the same velocity intervalas shown for instance in FIG. 13B. Preferably, the same velocityintervals are used for the characteristic curve as those described abovein the basic driving data set similarity measure.

Preferably, the characteristic vehicle relative energy consumptionfunction is calculated based upon a plurality of basic historic groupsof the above defined type, and specifically based upon a respectivevalue of said relative instantaneous vehicle energy consumption for theprevious-trip data sets belonging to the respective basic historic groupin question.

In particular, this pertains to each individual vehicle velocity valueused for calculating the function, or to each velocity interval coveredby the function, as applicable.

The average value is preferably a geometric average. In case basichistoric groups are used, as described above, wherein the said relativeenergy consumption-based group performance measure is calculated, thegroup performance measure can be used, preferably as it is, as therelative energy consumption for calculating the characteristic curve.Furthermore each relative energy consumption value in the characteristiccurve, or each characteristic curve, is also preferably calculated as aweighted average, so that more frequently updated basic historic groupsare given larger weight than less frequently updated basic historicgroups.

It is preferred that the basic parameter set does not comprise aparameter indicating the vehicle type, such as a VIN (VehicleIdentification Number) of the vehicle in question, but that the only wayof characterising the vehicle is using the above described collectionsand classes.

Fourth Aspect

According to one aspect of the present invention, in order to be able toprovide as relevant data as possible when calculating the said drivingperformance parameter, at least some, preferably substantially all, mostpreferably all of said previous-trip data sets are classified into a setof collections. In the above described case in which basic historicgroups are used, these collections are used in addition to the basichistoric groups, and the previous-trip driving data sets are henceclassified into both a respective basic historic group and a respectivecollection. As will be described in the following, this may take placeby each collection comprising its own set of basic historic groups,which sets may then be overlapping between different collections, and byeach previous-trip data set first being classified into a collection andthereafter into a basic historic group within that collection.

It is preferred that each of said collections only comprisesprevious-trip driving data sets for a particular class of vehicles, andthat all previous-trip data sets of one and the same vehicle areclassified into one and the same collection based upon a basic classconformity measure between driving data sets for the vehicle in questionand a set of class-defining parameters. In other words, each vehicle canbe characterised based upon driving data sets observed for that vehicle.In particular, such driving data sets can be used to determine to whatvehicle class that vehicle belongs.

The basic class conformity measure is hence a measure of the conformityof a number of individual driving data sets for one and the same vehicleto a particular vehicle class, based upon the said class-definingparameters for the class in question. After a vehicle has beenassociated with a particular class, each driving data set for thevehicle in question is then allotted to the same collection, namely thecollection corresponding to the vehicle class to which the vehicle isassociated. In case a vehicle is associated to a particular class at onepoint, and is then reclassified to a different class at a later, secondpoint, the previous-trip driving data sets collected for that vehicleand already allotted to the collection corresponding to the previouslyassociated class can either be reclassified into the collectioncorresponding to the new associated class, or alternatively only newlycollected previous-trip driving data sets can be allotted to the newcollection.

Each collection may correspond to exactly one class of vehicles, andvice versa.

According to this aspect of the current invention, illustrated in FIG.7, before calculating the above-described energy consumption-based tripperformance parameter, the current vehicle is classified into aparticular one vehicle class, in the following denoted the “currentclass”, of said set of classes, based upon the said basic classconformity measure. Then, the collection or a collection (the “currentcollection”) corresponding to the current class is identified. Thecurrent collection preferably comprises all previous-trip driving datasets previously observed for all vehicles currently allotted to thecurrent vehicle class.

It is preferred that each current-trip driving data set is mapped, asdescribed above, to at the most one particular basic historic groupbelonging to the current collection, based upon the above describedbasic group conformity measure.

Then, an energy consumption-based trip performance parameter value iscalculated for the current trip. This calculation is preferablyperformed as described above, and in particular based upon respectiveenergy consumption-based performance parameter values for previous-tripdata sets only in the said current collection, as opposed to using allprevious-trip driving data sets. Hence, the calculation may be performedbased upon a set of basic historic groups to which the current-tripdriving data sets are mapped, and corresponding instantaneous relativeenergy consumption for such basic historic groups, as described above,but wherein said set of basic history groups have all been allotted tothe current collection. Hence, when mapping the current-trip drivingdata sets to basic historic groups as described above, in this case onlybasic historic groups of the current collection are considered.

Further according to this aspect of the invention, the said basicparameter set further comprises instantaneous vehicle velocity andinstantaneous engine rotation speed. Then, the said class-definingparameters comprise, for each class of vehicles, a characteristic enginerotation speed for a particular vehicle velocity. As indicated above,instead of, or in addition to, the said basic parameter set furthercomprising instantaneous vehicle velocity and instantaneous enginerotation speed, it may comprise instantaneous motor load andinstantaneous energy consumption. Then, the said class-definingparameters comprise, for each class of vehicles, a characteristic energyconsumption for a particular motor load.

By using instantaneous vehicle velocity and instantaneous enginerotation speed at each such vehicle velocity for defining vehicleclasses, and in particular by using only these data for defining vehicleclasses, a vehicle classification yielding surprisingly accurate resultsin terms of driving performance parameter values is achieved. This is inparticular the case when using a methodology as the one described hereinfor calculating and processing such parameter values. Furthermore, theinvention will produce relevant results even if one and the same vehicleis driven under very different conditions, such as with or without atrailer, on icy or dry roads, with various wind strengths, outdoorstemperatures, and so forth.

FIG. 6a illustrates an example of an empirically or experimentallymeasured curve C, defining a typical or average relationship betweenvehicle velocity and engine rotation speed, for a particular class ofvehicles. The curve C may be determined by, for instance, using alldriving data sets in the collection corresponding to the class inquestion, for each instantaneous vehicle velocity calculating an averageengine rotation speed.

At a particular point P, a particular instantaneous vehicle velocity VELcorresponds to a particular instantaneous engine rotation speed RS.Hence, P is an example of a class-defining parameter for that particularclass of vehicles. This point P could be calculated as an average enginerotation speed for all observed driving data sets in the collection inquestion, that is for vehicles in the class in question, and having theinstantaneous vehicle velocity VEL.

A class conformance measure could then, for example, be constructed andused as follows:

-   1) Construct a curve, corresponding to curve C, but for an    individual particular vehicle the conformance of which is to be    determined. The curve is constructed based upon driving data sets    observed for that vehicle, such as by taking the average observed    engine rotation speed for each observed velocity and then adjusting    a polygon function to best fit the achieved pairs of data points for    all velocities.-   2) For the particular velocity VEL, calculate the distance between    the constructed curve and the point P.-   3) In case the distance is smaller than a predetermined largest    allowable distance, the conformance measure turns out in the    positive, and the vehicle in question is allotted to the class of    vehicles in question.

FIG. 6b illustrates a more complicated, and preferred, embodiment, inwhich the said class-defining parameters comprise, for each class ofvehicles, a respective characteristic engine rotation speed for aplurality of vehicle velocities. In this exemplifying case, the velocityaxis is divided into a series of non-overlapping intervals, preferablythe same as the above-discussed non-overlapping intervals for mappingdriving data sets into basic historic groups. Then, for each suchinterval, a corresponding allowed engine rotation speed interval isdefined. In FIG. 6b , these engine rotation speed intervals are of equallength, but they may also, for instance, be broader for vehicleintervals for which there are fewer observed previous-trip driving datasets in the collection in question.

Then, a class conformance measure could be constructed and used asfollows:

-   1) For a respective vehicle velocity point P1, [ . . . ], P11 in    each velocity interval, calculate an average engine rotation speed    for the particular vehicle that is to be classified. This value can    be calculated using interpolation in addition to averaging, in case    data is not available for the particular vehicle velocity value in    question. In this case, the engine rotation speed intervals can be    viewed as the class-defining parameters.-   2) For each point P1, [ . . . ], P11, calculate whether or not the    point is within the respective engine rotation speed interval.-   3) In case each point Pa, [ . . . ], P11, or at least a certain    predetermined proportion of the points, is or are within the    respective engine rotation speed interval, the conformance measure    turns out in the positive, and the vehicle in question is allotted    to the class in question.

From FIG. 6A, it is clear that the particular exemplifying vehiclerepresented by points P1, [ . . . ], P11 is allotted to the classrepresented by curve C and the illustrated set of engine rotation speedintervals.

It is realized that many different ways of performing such a conformancemeasurement between a particular vehicle and a particular collection arethinkable and possible. For instance, when there are many classes, oneparticular vehicle could be found to conform to several such classes. Inthat case, the conformance measure can further comprise a closenessmeasure, based upon which the single class to which the vehicle isclosest is the one to which the vehicle is mapped. This closenessmeasure may, for instance, comprise a measure of engine rotation speeddistance to the centre of each engine rotation speed interval for eachpoint P1, [ . . . ], P11, or another suitable measure.

According to one preferred embodiment, the class-defining parameters aremutable, and in particular dynamically updated as new driving data setdata becomes available. Hence, it is preferred that the class-definingparameters for a certain class, preferably for all classes or at leastsubstantially all classes, are automatically and dynamically updated inresponse to the collecting of previous-trip data sets, so that the saidcharacteristic engine rotation speed for a particular vehicle velocityin question is updated in response to the observation and collecting ofa set of instantaneous vehicle speed and instantaneous engine velocitydata values for a particular vehicle which has been classified into thecertain class. This may take place by identifying a corresponding basichistoric group in the collection, corresponding to the class inquestion, to which the previous-trip driving data set in question ismapped; updating that basic historic group; and then using the updatedbasic historic group together with other basic historic group involvingsimilar vehicle velocity data to update the said class-definingparameters. This way, the class definitions will automatically becomemore accurate as more data becomes available to the system.

When setting up a new system according to the present invention, it maybe so that no driving data set information is available. In that case, astandard set of initial vehicle classes, as defined by correspondingclass-defining parameters can be assumed as a starting point, afterwhich the class definitions may evolve over time as new data becomesavailable. Alternatively, the system uses a basic set of driving datasets, and the initial classes may be calculated based upon the saidbasic set of driving data sets, and then the class definitions mayevolve from there during the use of the system.

In either case, and also in other cases, from time to time a vehiclewill be observed, the driving data sets of which are quite far from theclosest class definition (as measured by said class conformity measure).In this case, it is preferred that the system may recognize this vehicleas belonging to a new vehicle class and as a reaction create such a newclass based upon the driving data sets collected for that vehicle. Thus,in case a particular vehicle is found to be further away from each ofsaid classes than a predetermined threshold distance, as measured by thesaid basic class conformity measure, an additional class is created,together with corresponding class-defining parameters and acorresponding collection. The class-defining parameters of the newlycreated class are then preferably calculated based upon theprevious-trip driving data sets observed for the vehicle in question. Itis further preferred that one single such vehicle, performing one singletrip during which it is observed to be far away from the closestexisting class, does not trigger the creation of a new vehicle class,but that at least a certain minimum number of vehicles and/or a certainminimum number of trips is required in order to actually launch the newvehicle class. The system may also comprise limitations for classcreation based upon trusted vehicles (see above). In the latter case, aminimum number of trusted vehicles may be required to be observed tobelong to a new class before such new class is actually created.

As the number of classes grows, it is preferred that the basic classconformity measure is adjusted correspondingly, so that each existingclass more narrowly defines the respective vehicle, for instance byusing more and more narrow velocity and/or engine rotation speedintervals of the type described above, so that the threshold, in termsof distance from the closest existing class, used as a requirement tolaunch a new vehicle class, becomes lower and lower. For instance,interval lengths may be calculated based upon the total number ofclasses and/or the total number of observed previous-trip driving datasets in the database 151.

This way, a more and more granular and fine-tuned set of vehicle classdefinitions will be created over time, as more data becomes available tothe system, in a way which is fully automatic and produces vehicleclasses that actually correspond to the main types of vehicles that usethe system. It is noted that no a priori knowledge about such vehiclesis necessary to achieve these results.

FIG. 7 illustrates the above described methodology. In a first step, aninitial set of classes is defined. Then, previous-trip driving data setsare collected, for a particular vehicle, but over time for manydifferent trips performed by many different vehicles. For each suchobserved vehicle, the vehicle in question is mapped to the closestexisting class, as described above and based upon said previous-tripdriving data sets and the said class-defining parameters for therespective class. In case the vehicle was successfully mapped to aclass, the mapped class is updated, by the previous-trip driving datasets updating the basic historic groups of the corresponding collection,and the method loops back to collecting previous-trip driving data sets.On the other hand, in case the vehicle was found to be too far from theclosest class, it is investigated whether or not enough data indicatingthe motivation to create a new class has been collected, as describedabove. In case this is so, a new class is created, the class-definingparameters of which are based upon the collected driving data sets ofthe vehicle in question, possibly in combination with previous-tripdriving data sets observed and collected for additional vehicles thatare also used for the creation of the new class. Thereafter, the methodloops back again to the collecting of previous-trip driving data sets,using the updated set of class definitions.

Each observed vehicle is preferably mapped to a particular single classbefore a performance parameter is calculated for that particularvehicle, and the calculation of the performance parameter is preferablybased only on driving data sets of the corresponding collection.However, a reclassification of each vehicle may be achieved lessfrequently than each observed trip, preferably less frequently thanevery ten trips. However, the updating of the class-defining parametersof the class to which a particular vehicle is allotted is preferablyperformed at least in connection to the finalizing of each tripperformed by the vehicle in question.

According to one preferred embodiment, the class-defining parameters donot comprise information regarding vehicle gear used. The surprisingfinding of the inventor is that gear number information does notsignificantly improve the results, in terms of classification accuracyfor the purpose of producing relevant driving performance parametervalues. It is even so that, since the gear usage affects the enginerotation speed for a particular vehicle velocity, it is difficult topredict suitable class-defining parameter values for a particularvehicle, even in the case in which all technical data about the vehicleis known. Hence, in case a particular known vehicle type, such as anewly launched car model of a particular brand, it is preferred that anew vehicle class is created, if needed, automatically by simplyconnecting one or several cars of the newly released model, preferablymarked as “trusted”, and then allow the system to automatically discoverand define a new set of class-defining parameters for the car model inquestion, based upon the driving data sets observed for these vehicles.

According to one preferred embodiment, in addition to the above definedcollections of previous-trip driving data sets and/or basic historicgroups, there is a main collection defined, comprising respectiveprevious-trip driving data sets, and in particular basic historic groupscorresponding to, such as having identical respective definition as, allrespective basic historic groups comprised in all of the above describedcollections. In this case, a corresponding basic historic group in thesaid main collection is always updated with respect to its groupperformance parameter when a group performance parameter of acorresponding basic historic group in another collection is updated.Hence, data of the basic historic groups of the main collection reflectthe data of all basic historic groups in other collections. According toone preferred embodiment, the data of the basic historic groups in themain collection can be used instead of basic historic groups for aparticular other collection under certain conditions. For instance, asuitable class may not exist to which the current vehicle can be mapped,or the current collection may not have enough updated group performancedata in order to produce a reliable result.

Fifth Aspect

According to one aspect of the invention, the above-discussed tripperformance parameter value is calculated as a first trip performanceparameter value in a way which is similar to the trip performanceparameter value calculation methodology described above in connection toFIG. 4. In fact, everything described in connection to FIG. 4 isrelevant also to this aspect of the invention, as applicable.

This present aspect is further illustrated in FIG. 9, wherein it isshown that, in a first step the previous-trip and current-trip drivingdata sets are collected, as described above.

Then, for each of the collected current-trip data sets, at least onecorresponding collected previous-trip data set is selected based uponthe above described basic driving data set similarity measure, which isarranged to measure similarity between driving data sets, and/or theabove described basic group conformity measure, which is arranged tomeasure conformity for a current-trip data set to a basic historic groupof previous-trip data sets. Hence, according to one embodiment,current-trip driving data sets are selected by mapping to individualprevious-trip driving data sets, and these selected previous-tripdriving data sets are used for the subsequent calculations. However, itis preferred that the above described mechanism using basic historicgroups, preferably also using the above described classes andcollections, is employed.

Thereafter, a relative instantaneous vehicle energy consumption value iscalculated for said selected corresponding previous-trip data set orsets, which relative energy consumption is relative to a total energyconsumption for a respective trip during which the previous-trip dataset in question was observed. It is understood that, in case the saidbasic historic groups are used, the selected previous-trip driving datasets are the ones comprised in the basic historic groups to which thecurrent-trip driving data sets were mapped. In particular, it ispreferred that the said relative instantaneous vehicle energyconsumption value is calculated for the respective previous-trip datasets in a basic historic group to which the current-trip data set inquestion is mapped, in relation to a total energy consumption for thecomplete trip during which the previous-trip data set in question wasobserved, and further preferably based upon an average value of saidrelative instantaneous energy consumption values for mapped respectivebasic historic groups.

In a last step, the said first current-trip driving performanceparameter value is calculated based upon an average value of saidcalculated relative instantaneous energy consumptions.

Hence, the respective relative energy consumption for each individualprevious-trip driving data set is a measure of the relative “goodness”,in terms of low energy consumption, that the previous-trip driving dataset in question was associated with during the previous trip inquestion. Similarly, the respective group performance parameter valuefor each historic group, is a measure of the “goodness” thatprevious-trip driving data sets that have a similar footprint in termsof basic parameter set values on average are associated with. Then, thefirst performance parameter is a measure of the average such “goodness”associated with previous-trip driving data sets, or basic historicgroups, which are similar to the collected current-trip driving datasets.

Hence, by breaking a current trip apart into a large multitude of smallcurrent-trip driving data set observation fragments, associating themwith previously observed such fragments and calculating the first tripperformance parameter value in the way illustrated in FIG. 9, the aboveadvantages in terms of automatically and accurately assessing comparabledriving performance with little a priori knowledge and under shiftingconditions are achieved, and the resulting first trip performanceparameter value constitutes an easily accessible, numerical value thatis directly useful as a trip performance measure. Hence, the tripperformance parameter value can be displayed to the driver during orafter the current trip, as described above, but it can also readily beused for making direct comparisons between different trips and drivers,and even between different vehicles, since the first trip performanceparameter is generally independent of driver and driving conditions. Inparticular, in case the mechanism using classes and collectionsdescribed above is used, the first performance parameter value will alsobe generally independent upon vehicle type, so that a trip using a busis readily comparable to a trip using a small car.

The portable electronic device 130, or alternatively the current vehicle100 itself, is preferably arranged with a piece of software arranged topresent to the driver of the current vehicle a graphical user interface,in turn arranged to present information comprising a representation ofthe calculated first trip performance parameter value for the currenttrip, and possibly also for previously conducted current trips for thesame driver, and possibly also for other previous trips.

The said portable electronic device 130 software may be a piece ofsoftware executable by or from the portable electronic device 130, suchas a locally installed and executed application, a remotely executedapplication, such as a web page application accessed from the portableelectronic device 130, or any other suitable type of software.

In the preferred case in which the first trip performance parameter iscalculated repeatedly, by central sever 150 or local server 160, duringthe current trip, based upon the so far collected current-trip drivingdata sets, it is preferred that a representation of an updated firstparameter value is presented to the driver on said graphical userinterface during the current trip.

It is furthermore preferred that the calculated first trip performanceparameter values calculated for previous trips are stored in thedatabase 151 and are made available via a suitable applicationprogramming interface (API) provided by the central server 150. Thisway, the manager of a fleet of transport vehicles, or similar, canfollow the progression of the fleet in terms of driving performance overtime, and perform analyses based upon first trip performance parametervalue data for the fleet.

In a particularly preferred embodiment, the first performance parameteris used to calculate a benchmark value. Once a plurality of firstperformance parameter values have been calculated, preferably for aplurality of different trips with a plurality of different vehicles andby a plurality of different drivers, the system determines a thresholdfirst trip performance parameter value such that only a minorpercentage, such as 10%, of all calculated trip performance parametervalues, are better than the said threshold value. Then, each newlycalculated first trip performance parameter value can be compared to thebenchmark value to see how far from the 10% top previous tripperformances that the current trip was.

It is furthermore preferred that the benchmark value is updated basedupon first trip performance parameter values calculated with respect tothe current trip, at least as long as the benchmark value has notconverged so that it substantially does not change with the updating ofnewly calculated trip performance parameter values. It is preferred thatthe benchmark value is made accessible throughout the system as a globalvariable.

Sixth Aspect

In one aspect of the invention, the value of a second trip performanceparameter is calculated, based upon several previously calculated tripperformance parameter values, preferably but not necessarily severalpreviously calculated trip performance parameter values of the aboveexplained type, namely the first trip performance parameter value.

In general, the second trip performance parameter value is calculatedbased upon the same data as the first trip performance parameter value,in terms of previous-trip and current-trip driving data sets, basichistoric groups, vehicle classes, collections, etc., as described indetail above. However, the idea behind the second trip performanceparameter value is more generally applicable than that of the first tripperformance parameter value. In particular, it is not strictlynecessary, albeit preferred, that the basic parameter set, for thepurposes of calculating the second trip performance parameter value,comprises instantaneous vehicle energy consumption. Instead, some otherparameter or combination of parameters comprised in the basic parameterset can be used to in some respect measure the relative quality of eachprevious-trip driving data set, such as parameters measuring tyre wear(for instance a suitable parameter combination of observed break usageand turning magnitude in relation to vehicle velocity). Such non energyconsumption-based parameters may be used in case the measurement aim isdifferent from the one described in detail herein below, but in case thebasic mechanism of the second trip performance parameter calculation,and its general advantages are still desired.

For reasons of simplicity, in the following the calculation of thesecond trip performance parameter will be described as if the basicparameter set comprises instantaneous energy consumption. In general,everything which is said in relation to the first trip performanceparameter herein is equally useful for the purposes of calculating andusing the second trip performance parameter value.

FIG. 10, which is similar to FIG. 9, illustrates the basic methodologyfor calculating the second trip performance parameter value according tothe present aspect of the invention. In a first step, current-trip andprevious-trip driving data sets are collected, as described above, andfor individual current-trip driving data sets correspondingprevious-trip driving data sets are selected. Thus far in the method,this aspect is in many regards the same as for the aspect describedabove in connection to FIG. 9.

However, for each selected previous-trip driving data set, a qualitymeasure is then calculated. This quality measure may be the abovedescribed relative energy consumption-based performance measure, but mayalso be something else.

Thereafter, a respective first trip performance parameter value iscalculated for each previous-trip data set, which first trip performanceparameter may be the same as the above-described first trip performanceparameter. However, it may also be another suitable type of tripperformance parameter, the value of which is calculated based upon thesaid quality measure. Preferably, the first trip performance parameteris a relative trip performance parameter arranged to measure therelative trip performance of the previous-trip driving data set inquestion in relation to the trip during which the previous-trip drivingdata set was observed. In the exemplifying case in which the qualitymeasure is a combination of instantaneous break and turn data, the firsttrip performance parameter for each previous-trip driving data set maybe a relative value for this instantaneous break/turn parameter ascompared to an average value of said parameter for the complete tripduring which the previous-trip driving data set in question wasobserved.

In a final step, the second trip performance parameter value iscalculated based upon the respective values of the said first tripperformance parameter for each of the selected previous-trip data sets.

FIG. 11 illustrates a method according to the present aspect of thepresent invention, in particular in which the above described basichistoric groups are used for the calculation of the second tripperformance parameter.

Hence, in a first step, for many previous trips, such as for at least100 previous trips, preferably at least 1000 previous trips, respectiveprevious-trip driving data sets are collected. For each suchprevious-trip driving data set, a relative quality measure iscalculated, preferably relative to a total quality for the completeprevious trip during which the previous-trip driving data set inquestion was collected, in particular preferably the above describedinstantaneous relative energy consumption.

In a second step, each previous-trip driving data set is mapped to abasic historic group.

In a third step, for each such mapped basic historic group, a respectivegroup performance parameter value, preferably the above described energyconsumption-based group performance parameter, is updated using thecalculated relative quality measure.

In a fourth step, a first trip performance parameter value, preferablythe above described energy consumption-based one, is calculated for eachof said many previous trips, based upon the updated group performanceparameters for the mapped respective basic groups.

It is noted that, in this fourth step, each previous trip can beregarded as a current trip, and that the first trip performanceparameter value then corresponds to the above described trip performanceparameter calculated for a current trip.

In a fifth step, again for all of said many previous trips, a respectivegeneral group performance parameter is updated for each of therespective mapped basic historic groups corresponding to theprevious-trip driving data sets observed during the previous trip inquestion, which update is based upon the updated respective first tripperformance parameter value calculated in the fourth step. Preferably,the general group performance parameter is an average value, such as ageometric average, of the respective first trip performance parametervalues previously calculated for all previous-trip driving data setsmapped to the basic historic group in question.

In a sixth step, current-trip driving data sets are collected for acurrent trip.

In a seventh step, each such current-trip driving data set is mapped toa respective basic historic group, in the way described above.

Then, in an eighth step, the second trip performance parameter value iscalculated based upon the respective general group performance parametervalues calculated for all basic historic groups to which current-tripdriving data sets are mapped. Preferably, the second trip performanceparameter value is calculated as an average value, such as a geometricaverage value, of the said general group performance parameter values.The said average value can also be a weighted average value, such as anaverage value in which more frequently updated basic historic groups aregiven more weight than less frequently updated basic historic groups. Incase not all current-trip driving data sets correspond to a respectiveexisting basic historic group, the averaging function may ignore thosecurrent-trip driving data sets for the purpose of calculating the secondtrip performance parameter value.

Using the present system and method for calculating, for a current trip,said second trip performance parameter in the above described wayachieves the surprising effect that the value of the second tripperformance parameter constitutes a very accurate measure of the risklevel assumed by the current driver. In other words, the second tripperformance parameter measures the risk behaviour of the driver. Apartfrom this aspect, all the advantages described above, in relation to thecalculation of the first trip performance parameter, also apply to thesecond trip performance parameter.

It is preferred that the second trip performance parameter is madeavailable to the current driver after or during the current trip in away which completely corresponds to the case for the first tripperformance parameter, as described above. It is also preferred that thesecond trip performance parameter values for individual drivers and/orcollectives of drivers are used for evaluation and risk assessmentpurposes. For instance, an insurance company may use the value of thesecond trip performance parameter as an input in the calculation of carinsurance premiums. Furthermore, such second trip performance parametervalue may be used to identify risk-assuming individuals or groups ofdrivers for the purposes of improving the operations of a transportcompany. There are numerous other ways in which such a measure of riskcan be used.

According to one preferred embodiment corresponding to FIG. 11, thecollected previous-trip data sets are classified into one of a pluralityof different predetermined basic historic groups based upon said basicsimilarity measure, and each current-trip data set is mapped to at themost one of said basic historic groups based upon said basic groupconformity measure. Then, each of the previous-trip data sets arefurther mapped to at the most one of said basic historic groups, at thetime comprising previous-trip data sets observed before theprevious-trip data set in question was observed, based upon said basicgroup conformity measure.

It is understood that the above-described preferred case in which thefirst (and consequently also the second) trip performance parameter iscalculated based upon a measure of the instantaneous energy consumptionof the previous vehicles, the qualified driving data parameters of theprevious-trip driving data sets comprise instantaneous energyconsumption. Then, the method comprises a step in which a relativeinstantaneous vehicle energy consumption value is calculated for saidprevious-trip data sets, which relative energy consumption is relativeto a total energy consumption for a respective trip during which theprevious-trip data set in question was observed. Furthermore, in thiscase the first trip performance parameter value is calculated based uponsuch calculated relative energy consumption values.

In particular, in this case it is preferred that, for the respectivebasic historic group to which each current-trip data set is mapped, andfor each further basic historic group to which each previous-trip dataset comprised in the basic historic group in question is in turn mapped,a respective relative instantaneous vehicle energy consumption value iscalculated, which relative energy consumption is relative to a totalenergy consumption for a respective trip during which the previous-tripdata set in question was observed. Furthermore, in this case each ofsaid first trip performance parameters is calculated based upon anaverage value of said relative instantaneous energy consumption values.

It is noted that, in case the methodology with vehicle classes andcollections described above is used, all calculations leading up to thesecond trip performance parameter value are limited to the currentcollection.

Seventh Aspect

In one aspect of the invention, illustrated in FIG. 14, current-trip andprevious-trip driving data sets are collected in a first step.Specifically, updated current-trip driving data sets are repeatedly readfrom the vehicle, wherein new such current-trip driving data sets areread from the vehicle at consecutive observation time points separatedby at the most a predetermined observation time period. This is similarto the above described aspects of the present invention. However, thecurrent-trip driving data sets in the present aspect each comprises datafrom at least a predetermined set of extended driving data parameters.

Furthermore, in the present aspect, the previous-trip driving data setsalso comprise the said extended driving data set parameters, and inaddition thereto the previous-trip data sets each comprises parametervalues for a predetermined set of a qualified parameters. The qualifiedparameter set, in turn, comprises the parameters of the above definedbasic parameter set as well as, or comprising, instantaneous vehicleenergy consumption.

Hence, the qualified parameter set at least comprises the basicparameter set. In case the basic parameter set does not compriseinstantaneous vehicle energy consumption, the qualified parameter setadds this parameter as compared to the basic parameter set. The extendedparameter set, in turn, comprises parameters that may or may not have anoverlap with the qualified parameter set, as explained below. To sum up,in this aspect each previous-trip driving data sets comprises value forthe basic parameter set as well as additional information.

The possible order of the various steps in this aspect is illustrated byarrows in FIG. 14.

Hence, in a second step, the collected previous-trip driving data setsare allotted to basic historic groups, as described above using the saidbasic driving data set similarity measure operating on the basicparameter set values of the previous-trip driving data sets.

In a third step, the value of a respective group performance parameteris then calculated for each basic historic group, in a way that may beas described above. Specifically, the group performance parameter may,but needs not, be a relative energy consumption-based performanceparameter as described above. It is preferred that the group performanceparameter value is calculated based upon only the qualified parameterset for each basic historic group.

In a fourth step, the said previous-trip driving data sets are groupedinto a set of historic extended groups of previous-trip driving datasets, such that each previous-trip driving data set is allotted to onesuch extended historic group. Like is the case for the above describedbasic historic groups, each such extended historic group is a group ofprevious-trip driving data sets. However, in contrast to the case forthe basic historic groups, previous-trip driving data sets are allottedto one of said set of extended historic groups based upon an extendeddriving data set similarity measure, which extended similarity measureis arranged not to take all values for said basic parameter set intoconsideration that are taken into consideration by the basic similaritymeasure. An extended group conformance measure may also be used, whichthen corresponds to the above described basic group conformance measure.

That the extended similarity measure is arranged not to take all valuesfor the basic parameter set into consideration that are taken intoconsideration by the basic similarity measure means that at least oneparameter of said basic parameter set is not used for calculating thevalue of the extended similarity measure for the purposes of groupingtogether previous-trip driving data sets in extended historic groups.However, it is preferred that none of the parameters in the basicparameter set is used for such calculation. Nevertheless, a certainparameter which forms part of the basic parameter set can be a parametermeasuring the same thing but in a different way, and hence count as notthe same parameter for these purposes. For instance, even if the basicparameter set comprises vehicle speed, and the previous-trip drivingdata sets comprises such a parameter value, measured on the actualvehicle engine or the wheel axis, the extended parameter set may alsocomprise vehicle speed, and the previous-trip driving data sets may thenalso comprise a vehicle speed value as measured using a GPS component inthe portable electronic device 130. It is noted that, even though theseparameter values correspond to the same metric, they are in general notnumerically the same, and are subject to different artefacts and errorsources. There are numerous other examples in which a certain metric canbe measured both directly on vehicle hardware and in some other way, forinstance using sensors of the portable electronic device 130 such asGPS, accelerometer, gyro, compass, etc. components. Hence, themeasurement method may be part of the definition of a “parameter”.

In a fifth step, for each of said collected current-trip data sets, thecurrent-trip data set in question is mapped to at the most oneparticular one of said extended historic groups, based upon an extendedgroup conformity measure between a driving data set and an extendedhistoric group.

The extended group conformity measure may be similar to the abovediscussed basic group conformity measure, in that it may, for instance,use predefined intervals for the extended parameter set values and allota certain current-trip driving data set to a certain extended historicgroup in case all extended parameter values of the current-trip drivingdata set fall within the corresponding interval of the extended historicgroup in question.

In a sixth step, a current-trip driving performance parameter iscalculated based upon the said group performance parameter valuescalculated for each respective basic historic group corresponding to theprevious-trip driving data sets comprised in the extended historic groupto which a current-trip data set was matched in the fifth step.

Hence, the extended historic groups, that are used in parallel to thebasic historic groups to classify collected previous-trip driving datasets, but that use the extended parameter set to perform theclassification as opposed to the basic parameter set, constitutes a linkbetween data measured on the vehicle and externally measured data ordata read otherwise not in direct contact with the vehicle as such. Bymapping current-trip driving data sets to external groups, it ispossible to use the information represented by previous-trip drivingdata sets in the same or corresponding way as described above, even incase the current-trip driving data sets do not comprise the basicparameter set and are therefore not possible to map to a particularbasic historic group based upon the above described basic similarity orconformance measure.

It is even preferred, in the present aspect of the invention, that thecurrent-trip driving data sets do not comprise said basic parameter set,at least to the extent to which sufficient data is lacking such thatusing the above described basic similarity and/or conformance measuresbecomes impossible. In some embodiments, it is enough that only onebasic parameter is lacking from the extended parameter set for such useto be impossible.

FIG. 15 is a more detailed view of an exemplifying embodiment of thepresent aspect.

Firstly, previous-trip driving data sets are collected for many previoustrips, and a relative quality (such as an energy consumption-basedquality in relation to a corresponding complete trip) is calculated foreach collected previous-trip driving data set, as described above.

Each collected previous-trip driving data set is mapped to a particularone basic historic group, based for instance upon the said basicconformity or similarity measure, and the respective basic groupperformance parameter is updated using the calculated relative qualityvalue.

Furthermore, each collected previous-trip driving data set is mapped toa particular one extended historic group, based upon said extendedsimilarity or conformance measure. For each such mapped extended group,a corresponding extended group performance parameter value is updatedusing a corresponding basic group performance parameter value taken fromthe basic group to which the previous-trip driving data set wasallotted.

Recalling that the basic group performance parameter may be a, possiblyweighted, average value of the relative quality measures calculated foreach previous-trip driving data set allotted to the basic historic groupin question, the extended group performance parameter may, in acorresponding way, be an average value of the corresponding basic groupperformance parameter values used to calculate the extended groupperformance parameter. In particular, it is preferred that the extendedgroup performance parameter is a weighted average value, wherein morefrequently used basic historic groups are given larger weight than lessfrequently used basic historic groups.

As a result, each extended group performance parameter will, over timeas the system is used, become a measure of average relative drivingquality for the basic historic groups to which the same previous-tripdriving data sets were allotted as were allotted to the extendedhistoric group in question.

When the current-trip driving data sets are then collected, they areeach mapped to a respective one extended historic group, and therespective extended group performance parameter values of the mappedextended historic groups are used to calculate a trip drivingperformance parameter value, which is then used as the trip performanceparameter values described above.

According to a present invention, the extended parameter set comprisesat least one parameter from a parameter list comprising GPS-basedvelocity, GPS-based acceleration, altitude, accelerometer-basedacceleration and compass-based heading. The said list may also comprisecorresponding changes over a predetermined time period, in a way whichcorresponds to the above described instantaneous vehicle velocity andinstantaneous vehicle velocity change, as well as to instantaneousengine speed and instantaneous engine speed change.

It is furthermore preferred that at least one of said current-tripdriving data set values, preferably all of the current-trip driving dataset values, are either registered by the current vehicle, which vehicleis connected to a central server via a wireless connection, or, evenmore preferably, registered by a portable device arranged in the currentvehicle, which portable device is connected using a wireless connectionto the said central server.

This way, a current vehicle which itself has no capability of recordingdata corresponding to the basic parameter set can still be used with thesystem, by recording data corresponding at least to the extendedparameter set, and then receiving a trip performance parameter valuewhich draws upon the total previous-trip driving data set pool collectedfor previous vehicles that in fact did have basic parameter set datareading capabilities. The only requirement is that such previousvehicles also recorded extended parameter set data during the previoustrips, so that it was possible to map the previous-trip driving datasets to appropriate extended historic groups.

In order to achieve the latter, it is preferred that the above-describedgraphical user interface providing piece of portable electronic device130 software is arranged to measure the complete extended parameter setdata during each current trip, and to report the measured extendedparameter set data to the central server 150 for processing. Suchmeasurement is preferably performed using sensor data locally availableto such piece of software, preferably using sensor hardware integratedinto the portable electronic device 130, even if could also be measuredby the vehicle itself, or by device 120. This way, a software serviceused by the users of the present system can automatically recordextended parameter set data, preferably in addition to basic orqualified parameter set data, in a way which is completely transparentto the user, for use by other current vehicles using the system.

In particular, it is preferred that the current vehicle is not arrangedto automatically provide driving data information via an externalinterface. In this case, the present aspect is namely particularlyuseful. For instance, the present method can be used in a car withoutany such external interface, or when a required piece of hardware 120 islacking or broken, by simply using a smartphone of the user or similar.The present invention is even useful for measuring driving performancefor non-motorized vehicles, such as bicycles, as long as enough relevantprevious-trip driving data sets with a relevant “driving quality”measurement have been recorded covering the currently used basicparameter set.

Hence, different systems may be implemented with particular adaptationsto suit particular vehicle types, wherein a relevant selections are madewith respect to basic, qualified and extended parameter sets, and thenotion of “driving quality”. For instance, “driving quality” for abicycle with an electrical help motor could be related to the use ofbattery pack power.

It is furthermore preferred that only trusted vehicles, as describedabove, are allowed to update the said extended group performanceparameter values. This will improve data quality.

In case collections are used, as described above, it is preferred thatthe basic historic groups used for the purposes of calculating theextended group performance parameter are taken from the above describedmain collection.

General

In general, it is preferred to use basic historic groups, as describedabove, that are used to store information regarding previous-tripdriving data sets mapped to such basic historic groups. Hence, it ispreferred to, as a part of each basic historic group, store and updatenot only the group performance parameter, but also the above describedtrip performance parameters for current trips during which acurrent-trip driving data set was mapped to the basic historic group inquestion, notably the first trip performance parameter. Such parameterdata which relates to the basic historic group as such is preferablyupdated dynamically as an average value of incoming data. For instance,this may be achieved by the currently updated parameter value beingstored in one memory position in the database 151, and the number ofprevious updates in an additional memory position in the database 151.Then, as a new updated arrives, the latter number can be increased byone, and the basic historic group parameter can be updated according tothe following, as an example:

${P_{N + 1} = \frac{{P_{N} \cdot N} + p}{N + 1}},$

wherein N is the number of previous updates; P is the stored, averageparameter value; and p is the incoming, new parameter value.

Apart from such average parameter values, it is furthermore preferred tostore additional information for each basic historic group. One exampleis when a traffic accident occurs. An accident can be confirmed invarious ways, such as by automatic detection based upon driving datasets, such as a rapid decrease in vehicle velocity followed by astandstill, or by manual registration. Once an accident has beenconfirmed, the system is preferably arranged to collect a predeterminednumber of current-trip driving data sets, and for each basic historicgroup to which the collected current-trip driving data sets are mapped,such as using the basic group conformance measure, update an accidentrisk parameter value stored for each such basic historic group. Thisupdate can be a simple counter which is increased by one each time thisoccurs for the basic historic group in question, or it may be an averagevalue updated as described above. Hence, such an accident riskparameter, when used in the system over a prolonged time period duringwhich a number of confirmed accidents occur, will be a measure of theprobability of each basic historic group being observed during a tripleading to an accident. Hence, a separate trip performance parametervalue can be calculated based upon the accident risk parameter valuesfor basic historic groups to which current-trip driving data sets aremapped during a current trip, or the above described second tripperformance parameter value can be calculated based at least partly uponsuch accident risk parameter values, in addition to the above describedcalculation, with the result of a trip performance parameter beingaccomplished which more accurately takes into consideration drivingbehaviour which is known to be risky.

Furthermore, the above described calculation of the second tripperformance parameter during the current trip may be used to, during thecurrent trip, predict a high risk of accidents for the current trip,based upon a poor value of the currently calculated second tripperformance parameter. In this case, the system is arranged to provide awarning to the current driver.

This latter is made possible by the general property of a systemaccording to the present invention to statistically relate drivingeffects on a macroscopic time scale to causes in terms of the drivingbehaviour on a microscopic time scale. This is a key insight of thepresent inventor.

It is noted, in connection to this calculation of the said accident riskparameter value, that a pattern of mapped basic historic groups ispreferably not identified and stored as such in connection to a accidentand for the purposes of identifying particularly accident-pronepatterns; instead, individual information is stored for each individualbasic historic group, and a trip performance parameter value is thencalculated based upon the individual basic historic groups and the saiddata. Since the number of basic historic groups is large, preferably atleast 100.000 basic historic groups, more preferably at least 1.000.000,the resulting trip parameter value will still in general be an accuratemeasure of the metric being measured, as applicable. This is true ingeneral for all the above-described aspects of the present invention.

The longer the system is used, the more data, in terms of updated basichistoric groups, each on average having been updated with manyprevious-trip driving data sets, the database 151 will contain. As aresult, resulting calculated trip performance parameters will becomemore and more accurate, even for current vehicles that have previouslynot been connected to the system. Hence, the system is, in this regard,a “learning” system in the sense that it is automatically improvedduring use.

Regarding the above-described trusted vehicles, according to onepreferred embodiment there is a parameter in the system which issettable so that certain selected trusted vehicles are associated withan increased weight when updating system data. Then, such trustedvehicles can be used to quickly adapt the system, in terms of vehicleclass definitions (class-defining parameter values) and basic historicgroup data (in particular group performance parameter values), when forinstance new car models are launched. In one preferred example, suchselected trusted vehicles are associated with an increased weight of atleast 5, preferably at least 10, times a default weight of a trustedvehicle. Hence, when previous-trip driving data sets are collected forsuch a trusted vehicle, this data counts as at least 5, preferably atleast 10, such previous-trip driving data sets collected at the sametime, and all parameter value updates are performed using this weight.As described above, this may lead to a new vehicle class automaticallybeing created for such a newly released car model, but it may also leadto an existing vehicle class definition being adapted to the new carmodel, depending on how different the new car model is from alreadyobserved previous vehicles. In one preferred embodiment, suchhigher-weight updates only apply to class-defining parameters, and notto, for instance, group performance parameter values.

According to one preferred embodiment, each basic historic group isassociated with a data quality parameter value, indicating the dataquality of the group performance parameter for the basic historic groupin question. According to one embodiment, such data quality parametercan indicate whether or not a previous-trip driving data set has beenmapped to the basic historic group in question. In case this is not sofor a particular basic historic group, the group performance parametermay be excluded from the calculation of the above described tripperformance parameters described above. This may, for instance, beaccomplished by the basic historic group in question being ignored forthe purposes of such calculation. In case fewer than a predeterminedpercentage of the observed current-trip driving data sets can be mappedto a respective basic historic group the data quality parameter of whichindicates a less than full quality, this may be indicated by the system,for instance by displaying a warning to the user of the current vehicleindicating that the calculated trip performance parameter is ofpotentially poor quality. The said predetermined percentage ispreferably between 50% and 90%. Alternatively, the relative percentageof full-quality basic historic groups can be used to calculate aconfidence interval with respect to a calculated trip performanceparameter, and then displayed to the current driver.

Once a previous-trip driving data of a trusted vehicle set is mapped tothe basic historic group in question, the data quality parameter may beupdated, so as to reflect a higher state of data quality. According toone preferred embodiment, the group performance parameter is updatedeven before this happens, based upon previous-trip driving data sets ofnon-trusted vehicles that are mapped to the basic historic group inquestion. Then, once the data quality parameter is set to indicate ahigher data quality, the hence updated group performance parameter willbecome available for use when calculating trip performance parameters.This approach has turned out to provide accurate trip performanceparameter values while still keeping the system simple yet dynamicallyadaptive.

Furthermore, as described above, the basic historic groups of differentcollections may be updated as a result of collected previous-tripdriving data sets for vehicles of different vehicle classes. This, inturn, will in general lead to different collections comprisingdifferently frequently updated group performance parameters, and data ofdifferent collections hence having different data quality. In this case,it is preferred for the system to comprise functionality forperiodically investigate whether basic historic groups have data qualityparameters that are set to indicate higher data quality, and for whichcorresponding basic historic groups of other collections do not havedata quality parameters that are set to indicate higher data quality. Ifthis is found to be the case, the respective group performance parametervalues of basic historic groups with lower data quality may be updatedusing respective group performance parameter values of correspondingbasic historic groups, in other collections, with higher data quality.“Corresponding” basic historic groups, in this context, preferably meansbasic historic groups with identical definition. The update is in thiscase preferably performed as a weighted average calculation of thelower-quality group performance parameter, wherein the weight of thehigher-quality group performance parameter is lower than the weight ofthe lower-quality group performance parameter. Preferably, such updatesbetween different collections only takes place between collections thecorresponding classes of which are more similar than a predeterminedvalue, which similarity is measured and calculated using a certainvehicle class similarity measure. This class similarity measure isarranged to measure similarity between two vehicle classes based uponthe respective class-defining parameters of the classes in question.

A similar method can be used when a new class is defined. In this case,a set of basic historic groups can be copied from the collectioncorresponding to another class which is sufficiently “close” the newlycreated class to the collection corresponding to the new class, whichset of basic historic groups have full data quality as indicated by saiddata quality parameters. In this case, it is preferred that therespective group performance parameters of such copied in basic historicgroups are given less weight than normal during data updates in thecollection corresponding to the newly created class, so that theconvergence of the collection is quicker as driving data sets are beingcollected for vehicles of the newly created class. Furthermore, it ispreferred that a characteristic vehicle velocity function is copied fromsuch a “close” class, and used for the newly created class. Thereafter,the copied characteristic function will be updated by collected drivingdata sets for the newly created class vehicles.

As described above, it is preferred not to store a pattern of basichistoric groups to which driving data sets have been mapped during atrip resulting in an accident. However, there are cases in which apattern of mapped basic historic groups is identified, and even stored.

One such example is for driver identification. It has turned out thatthe basic historic groups to which current-trip driving data sets aremapped for each particular driver follows a statistical pattern whichmay be different enough between drivers so as to be used for driveridentification. Hence, according to one preferred embodiment, the driverof each previous vehicle is identified, and a respective statisticalpattern of mapped basic historic groups, corresponding to collectedprevious-trip driving data sets, is identified and stored for eachdriver, for several previous trips made by each such driver. Then, thecurrent driver can be identified by a statistical comparison between thestored statistical patterns for the users and the pattern of mappedbasic historic groups during the current trip. Such comparison can beperformed in any manner which is conventional as such, and wouldtypically result in one of said stored statistical patterns thatrepresents the best match to the pattern produced during the currenttrip.

A “pattern”, as used herein, may comprise information both regarding theidentity of mapped basic historic groups; data contents of mapped basichistoric groups; and/or mapping frequency of basic historic groups; orany combination of such parameters. It is preferred, when determiningsuch a pattern, that data from several previous trips, such as at least20 previous trips, of the same driver are used for such determination;and also that the previous-trip driving data sets for such trips isfiltered so as to remove outlier data points.

In particular, in one preferred embodiment, such driver identificationmay be used to automatically stop a vehicle, or set off an alarm, if adriver which has not been previously authorized to drive the currentvehicle drives the current vehicle. To this end, the system may comprisea piece of hardware in the current vehicle arranged to stop the vehiclein a suitable way, such as after providing repeated warnings to thedriver.

In another preferred embodiment, driver identification data is stored inthe central database 151 for all or some previous trips, and may be usedto retroactively map particular drivers to particular previous trips,for instance for the purpose of automatically updating driving journals,to produce driving statistics or to investigate who drove a particularvehicle during a particular previous trip for insurance purposes. Thestored data may also, for instance, be used to verify that a particularperson is actually the driver in a motor competition or similar.

In one preferred embodiment, a pattern for a particular driver is usedirrespectively of which vehicle and which vehicle class is used duringthe current trip.

In an application similar to the above described pattern determination,basic historic groups mapped by previous-trip driving data sets observedfor the driver in question are analysed, and it is identified in whatbasic parameter set intervals, such as in what velocity intervals, thegroup performance parameter values of the said mapped basic historicgroups corresponding to those intervals are lowest. Then, this intervalinformation is presented to the user, and used to direct the attentionof the user to certain fields of improvement regarding the user'sdriving skills.

In one preferred embodiment, the current vehicle does not have thecapability to produce user-readable fuel-consumption data during thecurrent trip. In this case, the system is arranged to calculate the fuelconsumption for the current trip based upon relative energyconsumption-based group performance parameter values for basic historicgroups to which current-trip driving data sets are mapped. Thiscalculation is straight-forward but depends on the detailedimplementation of said performance parameter. The present inventor havediscovered that such calculated fuel consumption may be surprisinglyaccurate, even in the case in which the basic parameter set does notcomprise fuel consumption and when there is no fuel consumption valueavailable for readout from the current vehicle.

In one preferred embodiment, the current driver is an automated driver,such as a software- and/or hardware implemented robot or autopilot.

Further applications for the present invention is to assessing howdifficult it is to drive a certain stretch of road, in relative terms ascompared to other stretches of road and with respect to energyconsumption and driving riskiness, by performing a number of trips alongthe road in question and noting an average first and/or second tripperformance parameter value for such trips in relation to averagecorresponding trip performance parameter values for other stretches ofroad.

EXAMPLE

FIGS. 16 and 17 illustrate an example of an embodiment of the presentinvention, for a more detailed understanding of the same.

In FIG. 16, a series of observed and collected previous-trip drivingdata sets are shown (FIG. 16, top) along a time axis. The driving datasets are observed at consecutive time points, one second apart, startingat time=102 seconds. Each previous-trip driving data set comprises thefollowing data values, which are made available by the previous vehicleduring driving and for readout as described above, and communicated tothe central server 150 (and/or to the local server 160, as the situationmay be):

-   -   A predetermined qualified parameter set comprising        -   A predetermined basic parameter set, in turn comprising            -   Instantaneous vehicle velocity, “Vel” (km/h)            -   Instantaneous engine rotation speed, “RPM” (RPM)            -   Instantaneous vehicle velocity change as measured from                the observation point and forwards 5 seconds, “ΔVel”                (km/h)            -   Instantaneous engine rotation speed change as measured                from the observation point and forwards 1 second, “ΔRPM”                (RPM)        -   Instantaneous fuel consumption, “FC” (litres per 10 km)

As seen in FIG. 16, the vehicle velocity measured by the previousvehicle increases, from time point 102 to time point 107, from 82 to 85.At the same time, the engine rotation speed increases from 1550 to 1750.These shifts are also reflected in the change parameter values. In isrealized that the actual processing of the previous-trip driving dataset observed at time 102 will not actually be performed by the central150 or local 160 server until time 106, when the velocity change isknown.

Simultaneously as the above data is collected from the previous vehicleitself, previous-trip extended driving data sets (see FIG. 16, bottom)are also collected by a smartphone 130 held in the previous vehicle bythe driver (in this exemplifying embodiment). The observation timepoints are identical (time points 102-107, with 1 second apart), but theextended driving data observed, collected and communicated to the serverin question comprises the following data values:

-   -   A predetermined extended parameter set comprising        -   Instantaneous GPS-based velocity, “GPS-Vel” (km/h)        -   Instantaneous altitude, “Alt” (meters above sea level)        -   Instantaneous GPS-based velocity change as measured from the            observation point and forwards 5 seconds, “ΔVel” (km/h)        -   Instantaneous altitude change as measured from the            observation point and forwards 1 second, “ΔVel” (meters)

It is noted that the extended parameter set does not compriseinstantaneous fuel consumption.

Hence, in a step A, the previous-trip driving data set at time point 102is collected and communicated to the central server 150. In a step B,the driving data set in question, or more precisely, the velocity- andengine rotation speed data comprised in the data set, is used to updatea current characteristic engine rotation speed to velocity curve for thepresent class of vehicles. In a step C, the class conformance measure isused to map said characteristic curve to one particular of a set ofavailable and dynamically updated vehicle classes. In step D, theidentified class is used to find a corresponding collection, amongseveral such collections (displayed as circles in FIG. 16), eachcorresponding to a particular one of said vehicle classes. In thepresent example, the vehicle class may cover, for instance, middle-sizedstation wagon cars. It is noted that the present system has groupedthese vehicles together in such a class completely automatically,without any presupposed knowledge about how to group vehicles or vehicleproperties.

Then, in a step E, the previous-trip driving data set in question ismapped, using the group conformance measure, to a corresponding basichistoric group, among many such available groups in said collection. Inthe present example, the velocity and velocity change values aremeasured in 1 km/h intervals; and the engine rotation speed and enginerotation speed change are measured in 50 RPM intervals, which is thesame as used for basic historic group definitions, which is hence alsobased upon the same interval sizes. Therefore, the mapped basic historicgroup contains the same basic parameter set data as the previous-tripdriving data set. This provides for very rapid lookup functionality inthe system, in particular when using said classes and collections.

In a later performed step F, the previous-trip driving data set observedat time 103 is processed in a similar way, and is mapped to anotherbasic historic group.

it is preferred that the vehicle is not allowed to change vehicle classduring a trip. As a result, step B may be performed for allprevious-trip driving data sets at a single, later time.

After step E, in a step G, the mapped basic historic group in questionis updated with respect to its group performance parameter (GPP) value.This step is in fact taken after the previous trip is finished, oralternatively, if performed during the previous trip, under theassumption that the previous trip up to the time 102 is the totalprevious trip. For the total previous trip then, the total average fuelconsumption is read from the previous vehicle, and the instantaneousfuel consumption, namely 7.5 litres per 10 km, for the previous-tripdriving data set in question is divided by the said total average fuelconsumption. The result is a percentage value, indicating the relativefuel consumption at the small time window of 1 second at time point 102,as compared to the whole trip. In this case, the average fuelconsumption for the previous-trip driving data set in question was lowerthan previously noted (on average) for that particular basic historicgroup in that particular collection, why the GPP of the basic group isdecreased from 105.31 to 105.27, meaning that the average relative fuelconsumption for previous-trip driving data sets previously mapped tothat basic historic group is now 105.27%.

This is performed for all previous-trip driving data sets of theprevious trip in question, or at least intermittently and under theassumption that the previous trip up to a particular previous-tripdriving data set constitutes the total previous trip. Then, in a step H,the respective GPP values for all mapped basic historic groups for thetotal previous trip are summed, and an average GPP value is calculatedfor the trip. This value, which is the first trip performance parameter,is communicated to the device 130 and presented to the previous driver,preferably in relation to a benchmark value for first trip performanceparameters as determined based upon corresponding calculations forpreviously performed previous trips (see FIG. 17).

In a step I, the general group performance parameter (GGPP) of eachmapped basic historic group is also updated, using the said calculatedfirst trip parameter value. In this case, the first trip performanceparameter value turned out to be 104.85, which is higher than the GGPP(98.11) of the basic historic group in question. Hence, its GGPP valueis averaged up to 98.13. The corresponding is done for all mapped basichistoric groups. Then, the second trip performance value is calculatedby averaging all GGPP values for all mapped basic historic groups, andthe second trip performance parameter is also communicated to the device130 for display to the previous driver (FIG. 17).

Also, in a step J, for each mapped basic historic group, a maincollection basic historic group is also identified, using the groupconformance measure, and its GPP and GGPP measures are updated in a waywhich corresponds to the mapped basic historic groups of the collectioncorresponding to the vehicle class to which the previous vehiclebelongs. It is noted that the mapped main collection basic historicgroup has GPP and GGPP values that are different from those for thecorresponding non-main collection group, due to the fact that they havebeen updated historically using different previous-trip driving datasets.

In a step K, the extended previous-trip driving data set is collected attime point 102, that is the same or corresponding observation time pointas the above described previous-trip driving data set. The extendeddriving data set is mapped, using an extended group conformance measure,to a corresponding extended historic group, among a set of many suchextended historic groups. This mapping entirely corresponds to themapping to the basic historic groups, as described above, and is alsobased upon identical intervals. The mapped extended group is furthermapped to the main collection basic historic group described above,using the knowledge available to the system that the basic parameter-and extended parameter previous-trip driving data sets, respectively,were collected at the same time (at time point 102).

Then, in a step N, a GPP value of the mapped extended group is updatedusing the GPP value of said mapped main collection basic historic group,using an average function corresponding to the ones described above.Hence, the GPP value of the extended group is updated, based upon theGPP value 102.89 of the basic group, from 101.42 to 101.44, reflectingthe fact that the GPP value of 102.89 is higher than 101.42.

In the case the previous trip is seen as a current trip, the same stepsA-J are performed, with the goal of not only updating the basic historicgroup data, but also to calculate said first and second trip performanceparameter values for presentation to the current driver.

In the particular case in which the current vehicle does not offer fuelconsumption data, the updates in steps G, I and J are not performed,since there is no data available for doing those updates. However, thefirst and second trip performance parameters may still be calculated andpresented to the current driver.

In case the previous or current vehicle is not a trusted vehicle, theupdates are performed but a respective quality flag on each historicbasic group may not be set to indicate full quality.

In the particular case in which no basic parameter data is available forreadout from the current vehicle, steps A-J are not performed at all.Instead, steps K, L and M are performed, and the first and second tripperformance parameters are calculated based upon the mapped extendedgroups corresponding to each collected extended previous-trip drivingdata set. The update in step N is not performed in this case.

Hence, for a current vehicle without an interface providing data on fuelconsumption, the system may calculate trip performance values for thecurrent trip. Even for a current vehicle without any readable datawhatsoever trip performance values can be calculated for the currenttrip, as long as the extended data set is available for collection via asmartphone or other device present in the vehicle during the currenttrip.

FIG. 17 illustrates the screen 132 of device 130 (or any other screen inthe current vehicle or elsewhere which is accessible to the currentdriver during or after a current trip) at the time of presenting thesaid information. On the screen 132, information (BM=benchmark value,the historic 10% top performing trips with respect to first tripperformance parameter; Efficiency=first trip performance parameter valuefor the current driver; and Risk=second trip performance parameter valuefor the current driver) provided from the central server 150 (or thelocal server 160, as the case may be) is displayed along a time axis(X-axis; the parameter values are displayed on the Y-axis), with onerespective data point for each of the last three trips performed by thedriver in question, in this case regardless of what vehicle was used. Ascan be seen in FIG. 17, the current driver has improved somewhat duringhis or her last three current trips. The current driver has a slightlyhigher risk score than driving efficiency score, but still has some waysup to being among the best-performing drivers. At the same time, thedriver collective using the system has improved on average, increasingthe BM value slightly over time.

In case the methodology described herein is followed for calculating thesaid first and second trip performance parameters, it has turned outthat the first trip performance parameter is an accurate measure ofrelative driving efficiency, regardless of vehicle, and that the secondtrip performance parameter is an accurate measure of driving riskiness,also regardless of vehicle.

Above, preferred embodiments have been described. However, it isapparent to the skilled person that many modifications can be made tothe disclosed embodiments without departing from the basic idea of theinvention.

It is generally noted, that the above described seven aspects of thepresent invention are freely combinable in any constellation, andindividual details from one of said aspects are readily useful in any ofthe other aspects, as applicable.

In general, it is preferred that the present system does not perform anyanalysis of the current trip based upon geographic location of thevehicle or based upon map data. Instead, the system preferablycompletely relies upon current-trip driving data sets as collectedduring each trip and as compared to previous-trip driving data sets asdescribed above.

If the said basic historic groups are used, it is realized that theactual previous-trip driving data sets need not be stored in thedatabase at all. Instead, after a previous-trip driving data set hasbeen mapped to a basic historic group, and the basic historic group hasbeen updated, such as with respect to its group performance parameter,the previous-trip driving data set may actually be discarded and notstored. Then, the information comprised in the previous-trip drivingdata set lives on in the database in the form of the definition of thebasic historic group in combination with the updated group performanceparameter value.

Moreover, each driving data set parameter value may be aninstantaneously read value, or be measured over a certain small timeperiod, such as about 1 second, and averaged across that small timeperiod.

It is realized that vehicles of fundamentally different types, such asgasoline vehicles, completely electrical cars, boats, aeroplanes andbicycles, are preferably allotted to different instantiations of thepresent system, in order to achieve more relevant data comparisonsbetween different vehicle classes. However, it is also possible to onesingle system for all such different vehicle types, since the vehicleclasses will typically converge into a set of classes wherein differenttypes of vehicles are properly represented, as long as the basicparameter set, the qualified parameter set and the extended parameterset (as applicable) are carefully selected.

Hence, the invention is not limited to the described embodiments, butcan be varied within the scope of the enclosed claims.

Common Expressions and Definitions

Current trip=the trip which is performed by a particular driver and aparticular vehicle now, and for which a trip performance parameter valueis to be calculated.

Previous trip=a trip which was performed at least partially before thecurrent trip.

Predetermined set of basic driving data parameters=basic parameterset=standard data set provided by vehicle.

Basic data set=observed set of parameter data comprising the basicparameter set. Predetermined set of qualified driving dataparameters=qualified parameter set=basic parameter set as well asinstantaneous energy consumption.

Qualified data set=observed set of parameter data comprising thequalified parameter set.

Predetermined set of extended driving data parameters=extended parameterset=standard data set not entirely provided by vehicle.

Extended data set=observed set of parameter data comprising the extendedparameter set.

Current-trip driving data set=set of parameter data observed during thecurrent trip. A current-trip data set can be a current basic data set, acurrent qualified data set and/or a current extended data set.

Previous-trip driving data set=set of parameter data observed during aprevious trip. A previous-trip data set can be a previous basic dataset, a previous qualified data set and/or a previous extended data set.

Historic basic group of previous-trip driving data sets=basic historicgroup=the previous-trip data sets that are “similar” to each otheraccording to the basic similarity measure.

Historic extended group of previous-trip driving data sets=extendedhistoric group=the previous-trip driving data sets that are “similar” toeach other according to the extended similarity measure.

Basic driving data set similarity measure=basic similaritymeasure=comparison measure for basic and qualified parameter set datasets.

Extended driving data set similarity measure=extended similaritymeasure=comparison measure for extended parameter set data sets.

Basic conformity measure for a driving data set to a historic group ofprevious-trip driving data sets=Basic group conformitymeasure=conformity measure between a basic or qualified parameter setdata set and a basic historic group.

Basic conformity measure for the driving data sets for a particularvehicle to a set of class-defining parameters=Basic class conformitymeasure=conformity measure between a number of driving data sets for avehicle and a certain parameterized characteristic information for aparticular class of vehicles.

Extended conformity measure for a current-trip driving data set to ahistoric group of previous-trip driving data sets=Extended groupconformity measure=conformity measure between an extended parameter setdata set and an extended historic group.

Collection of previous-trip data sets=collection of previous-tripdriving data sets or basic historic groups for all vehicles belonging toa certain class of vehicles.

Current class=the class to which the current vehicle belongs.

Current collection=the collection corresponding the class to which thecurrent vehicle belongs.

Energy consumption-based group performance parameter=Energy-based groupperformance parameter=performance parameter calculated based upon energyconsumption for previous-trip driving data sets of a particular basichistoric group.

General group performance parameter=General group performanceparameter=performance parameter for a particular basic historic groupcalculated based upon respective values of energy-based groupperformance parameters for other basic historic groups.

First energy consumption-based trip performance parameter=Firstenergy-based trip performance parameter=performance parameter calculatedfor a current trip based upon energy consumption-based group parametersfor basic historic groups.

Second energy consumption-based trip performance parameter=Secondenergy-based trip performance parameter=performance parameter calculatedfor a current trip based upon first energy-based trip performanceparameter values for basic historic groups.

Current vehicle=the vehicle currently being driven, for which theperformance parameter is to be calculated

Driver=person or entity driving or controlling vehicle

Characteristic instantaneous relative energy consumption curve=Functiondescribing, for a particular vehicle class, a typical relationshipbetween instantaneous vehicle velocity and relative energyconsumption-based performance.

1. Method for automatically assessing performance of a driver of acurrent vehicle for a particular current trip, wherein updatedcurrent-trip driving data sets are repeatedly read from the vehicle,which current-trip data sets each comprises data from at least apredetermined set of basic driving data parameters, wherein new suchcurrent-trip data sets are read from the vehicle at consecutiveobservation time points separated by at the most a predeterminedobservation time period, wherein the method comprises the steps of a)collecting previous-trip driving data sets, observed at a plurality ofdifferent observation time points, for a plurality of different previoustrips made by a plurality of different drivers and a plurality ofdifferent vehicles, which previous-trip data sets each comprisesparameter values for at least a certain predetermined set of qualifieddriving data parameters in turn comprising the said basic parameter setand in particular instantaneous vehicle energy consumption andinstantaneous vehicle velocity; b) for a plurality of said previous-tripdata sets, calculating a respective relative instantaneous vehicleenergy consumption value, which relative energy consumption is relativeto a total energy consumption for a respective trip during which theprevious-trip data set in question was observed; c) calculating acharacteristic vehicle relative energy consumption function regardingthe value of said relative instantaneous vehicle energy consumption fordifferent instantaneous vehicle velocity parameter values; and d)calculating a value of a trip performance parameter based upon aweighted average value of the respective relative instantaneous energyconsumptions for previous-trip data sets that correspond to each of saidcurrent-trip data sets based upon a similarity or conformance measureregarding the respective values of said basic parameters, whichweighting is performed using said characteristic vehicle relative energyconsumption function.
 2. Method according to claim 1, wherein saidcharacteristic vehicle relative energy consumption function iscalculated based upon an average relative instantaneous vehicle energyconsumption for several previous-trip data sets having the same vehiclevelocity.
 3. Method according to claim 1, wherein said previous-tripdata sets are classified into one of a plurality of differentpredetermined basic historic groups based upon a basic similaritymeasure arranged to measure similarity between driving data sets, andwherein each current-trip data set is mapped to at the most one of saidbasic historic groups based upon said basic group conformity measure. 4.Method according to claim 3, wherein the said characteristic vehiclerelative energy consumption function is calculated based upon aplurality of such groups, and specifically upon a respective value ofsaid relative instantaneous vehicle energy consumption for theprevious-trip data sets belonging to the respective basic historic groupin question.
 5. Method according to claim 3, wherein said relativeinstantaneous vehicle energy consumption value is calculated for therespective previous-trip data sets in the basic historic group to whichthe current-trip data set in question is mapped, in relation to a totalenergy consumption for the complete trip during which the previous-tripdata set in question was observed.
 6. Method according to claim 1,wherein the method further comprises the step of classifying saidprevious-trip data sets into a set of collections, wherein each of saidcollections only comprises previous-trip data sets for a particularclass of vehicles, wherein the current vehicle is classified into aparticular current class of a set of classes based upon a basic classconformity measure between driving data sets for the vehicle in questionand a set of class-defining parameters, said class corresponding to acurrent collection of previous-trip driving data sets, wherein allprevious-trip driving data sets of one and the same vehicle areclassified into one and the same collection, based upon said basic classconformity measure; wherein the said trip performance parameter value iscalculated based upon only the said respective relative instantaneousvehicle energy consumption values for previous-trip data sets in saidcurrent collection.
 7. Method according to claim 6, wherein the saidbasic parameter set comprises instantaneous vehicle velocity andinstantaneous engine rotation speed, and wherein the said class-definingparameters comprise, for each class of vehicles, a characteristic enginerotation speed for a particular vehicle velocity.
 8. Method according toclaim 1, wherein said predetermined observation time period is at themost 10 seconds, preferably at the most 5 seconds, more preferably atthe most 2 seconds.
 9. Method according to claim 1, wherein parametervalues of said basic parameter set are automatically recorded by thevehicle and either communicated to a portable electronic device arrangedat the vehicle, which portable electronic device communicates, via awireless link, said parameter values to a central server, orcommunicated, via a wireless link, directly from the vehicle to saidcentral server.
 10. Method according to claim 1, wherein the said tripperformance parameter value is calculated by and communicated, via awireless link, from a central server to the vehicle, such as to aportable electronic device arranged at the vehicle, and presented to thedriver.
 11. Method according claim 10, wherein a value of said tripperformance parameter is calculated repeatedly, preferably at leastevery 10 minutes, more preferably at least every 2 minutes, morepreferably at least every 30 seconds, during the current trip, whereinthe current trip is considered to be the current trip up to the momentat which the value of the said trip performance parameter is calculatedand for the purposes of calculating the said trip performance parametervalue in question, and wherein the currently calculated such value iscommunicated to the vehicle and presented to the driver uponcalculation.
 12. Method according to claim 1, wherein each of saidprevious-trip driving data sets is observed at a respective one of saidplurality of different observation time points, and wherein the saidparameter values of the previous-trip driving data sets are each readeither as a respective instantaneous value or a respective average valueread across a certain respective time period of at the most 5 seconds oflength.
 13. System for automatically assessing performance of a driverof a current vehicle for a particular current trip, which system isarranged to repeatedly read updated current-trip driving data sets fromthe vehicle, which current-trip data sets each comprises data from atleast a predetermined set of basic driving data parameters, wherein thesystem is arranged to read new such current-trip data sets from thevehicle at consecutive observation time points separated by at the mosta predetermined observation time period, wherein the system comprises aserver, arranged to collect previous-trip driving data sets, observed ata plurality of different observation time points, for a plurality ofdifferent previous trips made by a plurality of different drivers and aplurality of different vehicles, which previous-trip data sets eachcomprises parameter values for at least a certain predetermined set ofqualified driving data parameters in turn comprising the said basicparameter set and in particular instantaneous vehicle energy consumptionand instantaneous vehicle velocity, wherein the server is arranged to,for a plurality of said previous-trip data sets, calculate a respectiverelative instantaneous vehicle energy consumption value, which relativeenergy consumption is relative to a total energy consumption for arespective trip during which the previous-trip data set in question wasobserved, wherein the server is arranged to calculate a characteristicvehicle relative energy consumption function regarding the value of saidrelative instantaneous vehicle energy consumption for differentinstantaneous vehicle velocity parameter values, and wherein the serveris arranged to calculate a value of a trip performance parameter basedupon a weighted average value of the respective relative instantaneousenergy consumptions for previous-trip data sets that correspond to eachof said current-trip data sets based upon a similarity or conformancemeasure regarding the respective values of said basic parameters, whichweighting is performed using said characteristic vehicle relative energyconsumption function.
 14. System according to claim 13, wherein thesystem is arranged to observe each of said previous-trip driving datasets is at a respective one of said plurality of different observationtime points, and wherein the system is arranged to read the saidparameter values of the previous-trip driving data sets either as arespective instantaneous value or a respective average value read acrossa certain respective time period of at the most 5 seconds of length.